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• Temporal information is effectively integrated into the representation model. • A new prediction model is proposed to gain temporal information. • The memory capacity of the replay buffer is smaller than the existing methods. • The policy lag is proven to be decreased quickly in maximum-entropy reinforcement learning. Partially observable Markov decision process (POMDP) is a key challenging problem in the application of reinforcement learning since it comprehensively describes real agent-environment interactions. Recent works mainly utilize conventional reward signals to train a representation that converts POMDPs to MDPs. However, rewards alone are not enough for a good representation without temporal information. In this paper, we first introduce a novel Recurrent Prediction Model to integrate temporal information into the representation that solves POMDP problems by training three additional unsupervised prediction models, named transition model, reward recovery model, and observation recovery model. This paper secondly makes a modification of the data structure of vanilla replay buffer to reduce the memory usage and thirdly proposes an off-policy correction algorithm to decrease the policy lag in POMDPs. The experiments show that our model achieves better performance in partially observable environments on both stand-alone and distributed training systems. [ABSTRACT FROM AUTHOR]

This paper studies model predictive security control (MPSC) for networked switched systems under denial-of-service (DoS) attacks. Most of existing works only adjust the triggering scheme when being attacked. Different from them, this paper proposes a novel timing-response event-triggering scheme (TR-ETS) to reduce the impact of attacks on system performance, which can not only configure system resources adaptively, but also accurately detect attack information and compensate the attacked data. Specifically, the proposed scheme includes two event-based triggers, which can dynamically and jointly regulate the communication/calculation ability, generate virtual attack sequences and acquire the number of passive packet loss. Then, based on the triggered states, a class of model predictive controllers is designed to optimize the control action. Due to possible strong attacks, a security control framework including network and local loops be introduced and a permissable type-switching mechanism (PTM) is used. Under the permissable controllers (i.e., network and local controllers), sufficient conditions for the stability of closed-loop switched systems are derived. In addition, a set of model predictive optimization algorithm using linear matrix inequalities (LMIs) technique is addressed. Finally, the effectiveness of the proposed method is verified by illustrative examples. [ABSTRACT FROM AUTHOR]

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed. • A user behavior prediction model is proposed. • Sparse representation can be used to express the rumor feature space. • The complex influences that influence user behavior are quantified. [ABSTRACT FROM AUTHOR]

• A review and comparison of surrogate models used in single-objective SAEAs. • Review surrogate models in terms of absolute and relative fitness models. • The construction and prediction time of models, and model's accuracy are compared. • SAEAs with different surrogate models are compared. Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or meta-models to approximate the fitness function in evolutionary algorithms (EAs), are effective and popular methods for solving computationally expensive optimization problems. During the past decades, a number of SAEAs have been proposed by combining different surrogate models and EAs. This paper dedicates to providing a more systematical review and comprehensive empirical study of surrogate models used in single-objective SAEAs. A new taxonomy of surrogate models in SAEAs for single-objective optimization is introduced in this paper. Surrogate models are classified into two major categories: absolute fitness models, which directly approximate the fitness function values of candidate solutions, and relative fitness models, which estimates the relative rank or preference of candidates rather than their fitness values. Then, the characteristics of different models are analyzed and compared by conducting a series of experiments in terms of time complexity (execution time), model accuracy, parameter influence, and the overall performance when used in EAs. The empirical results are helpful for researchers to select suitable surrogate models when designing SAEAs. Open research questions and future work are discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for the analysis and comparison of stochastic solvers based on the statistical distribution of their variables. The observed variables of the stochastic solvers are the runtime and number of function evaluations required to reach a (sub)-optimal solution. These variables were measured using a target approach. We extended the conventional approach, which usually predicts the average value of variables, by predicting their statistical distributions. If possible, we can predict, not only the average value, but also all the values of the observed variables according to the identified distribution and given probability. We can also predict the probability of reaching (sub)-optimal solutions according to the variable's values. The approach was empirically validated by comparing solvers for discrete and continuous problems. In our experiment, the differences between predicted and measured values of runtime, number of function evaluations and probability are 10% or less. This proves that the proposed approach is useful and can be used for the analysis and comparison of stochastic solvers. Although, the approach has some limitations, it can mitigate the issue of stochastic solvers being unable to provide (sub)-optimal solutions, and can be used to determine the stopping optimization criteria. • An approach to the comparison of stochastic solvers. • The predictive modeling based on optimal and sub optimal solutions. • The analysis of the distribution of runtime and number of function evaluations. • The predictive models based on distribution of solvers' variables are established. • The predictive models provide probability of reaching the optimal solutions. [ABSTRACT FROM AUTHOR]

Multi-label learning (MLL) copes with applications such as text classification and image recognition where each instance is associated with multiple labels. Currently, some label-specific feature conversion approaches such as LIFT serve each individual label well, however do not take advantage of label correlation. Some label correlation exploitation approaches such as GLOCAL have independent model prediction and label correlation stages, thus limiting their learning ability. This paper introduces LSTC, a new algorithm that generates label-specific features and exploits third-order label correlation. On the one hand, for each label, we choose the same number of positive and negative representative instances inspired by density peak. These instances are used to generate a new data matrix with label-specific features. On the other hand, we train a paired output prediction network for each label based on these matrices of its own and that of two auxiliary labels. In this way, third-order label correlations are implicitly exploited. Specifically, the two auxiliary labels are the most similar and least similar, respectively, to avoid getting stuck in local optima. Experiments are conducted on seventeen benchmark datasets in comparison with ten popular algorithms. Results on eight measures demonstrate the superiority of LSTC on data from various domains except the text-domain. The source code is available at github.com/fansmale/lstc. • We adopt density peaks under Gaussian kernel to extract label-specific features. • We train the model for each label using the data of two auxiliary labels. • We use the softmax function to obtain numerical predictions from paired outputs. • Our algorithm shows superiority in ranking-based measures of non-text data. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of optimal hyper-parameter configuration in relational topic models. The main goal is to validate the hypothesis that single-objective Bayesian Optimization (BO) can discover a hyper-parameter setting that leads a set of relational topic models to simultaneously ensure good prediction capabilities and significant topics from a qualitative perspective. Our research, as a result of a comparative analysis performed on 7 state-of-the-art models, 5 performance measures and 3 datasets, has highlighted three main findings: (1) the majority of relational topic models are not able to offer a good trade-off between classification capabilities and topic interpretability; (2) single-objective optimization of hyper-parameters, targeted on maximizing the F1-Measure, is able to create topics that are also optimal with respect to the Kullback Leibler divergence measure; (3) the Pareto frontiers across several performance metrics reveals that the most promising trade-off between the performance metrics can be obtained by Constrained Relational Topic Models. [ABSTRACT FROM AUTHOR]

Machine learning models, especially deep neural network (DNN) models, have widespread and valuable applications in business activities. Training a deep learning model for commercial use requires plenty of private data, expert knowledge, and computing resources. The huge commercial value of such trained models has attracted the attention of attackers. Attackers can construct a dataset by repeatedly querying the target model for the output of the requested samples and then train a substitute model on this dataset that functions similarly to the target model. In this paper, we propose a defense scheme based on physical unclonable function (PUF) against such black-box model extraction attacks. We deploy a PUF on the user side and the corresponding PUF model on the service provider side to ensure that only legitimate users can obtain the correct model predictions. Our experimental results show that by choosing a suitable fuzzy extractor threshold d , legitimate users can recover more than 99.5% of the prediction results with a little additional computational overhead to the service provider. We perform a model extraction attack in the most favorable case for the attacker, and the prediction accuracy of the obtained substitute model is only about 10%, which demonstrates the effectiveness of our proposed scheme. Compared to existing defenses, our scheme not only effectively prevents black-box model extraction attacks but also ensures that the accuracy of the prediction service for legitimate users is not affected. • Defense based on PUF obfuscation against black-box model extraction attacks. • A PUF and the corresponding PUF model ensure that legitimate users can obtain correct model predictions. • Legitimate users can correctly recover more than 99.5% of the predictions with a little additional computational overhead. • The prediction accuracy of the model extracted by the attacker is only about 10%. [ABSTRACT FROM AUTHOR]

• Emerging Mobile Health systems are examples of novel technologies. • Data are collected from sensor nodes and forwarded to local databases. • From cloud computing services, the data are collected for further analysis. • This paper presents a detailed overview of M-Health systems. • We propose a secure Android-based architecture to collect patient data. Nowadays, new highly-developed technologies are changing traditional processes related to medical and healthcare systems. Emerging Mobile Health (M-Health) systems are examples of novel technologies based on advanced data communication, deep learning, artificial intelligence, cloud computing, big data, and other machine learning methods. Data are collected from sensor nodes and forwarded to local databases through new technologies that enable cellular networks and then store the information in cloud storage systems. From cloud computing services or medical centres, the data are collected for further analysis. Furthermore, machine learning techniques are being used for accurate prediction of disease analysis and for purposes of classification. This paper presents a detailed overview of M-Health systems, their model and architecture, technologies and applications and also discusses statistical and machine learning approaches. We also propose a secure Android-based architecture to collect patient data, a reliable cloud-based model for data storage. Finally, a predictive model able to classify cardiovascular diseases according to their seriousness will be discussed. Moreover, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results show encouraging results in terms of the proposed predictive model for an M-Health system. Keywords: Machine Learning, Predictive, Models, M-Health, Classification, SVM, Decision Tree, Accuracy [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the short-term versus long-term performance of various particle swarm optimization (PSO) control parameter configurations. While evidence suggests that the best PSO parameter values to employ are time-dependent, this paper provides an in-depth examination of a small set of parameter values to provide a more concrete quantification of the performance degradation observed with specific control parameter configurations over time. Given that the short-term performance is not necessarily indicative of long-term performance, this paper proposes that machine learning techniques be used to build predictive models based on two easily-observable landscape characteristics. Finally, using the predictive models as a basis, this paper also proposes a parameter-free PSO algorithm, which performs on par with other top-performing PSO variants, namely the three best performing static PSO configurations, particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC), and particle swarm optimization with improved random constants (PSO-iRC). [ABSTRACT FROM AUTHOR]

• We propose a novel multi-step forecasting framework named MFTM-Informer. • Pattern matching is extended to multivariate scenarios for extracting systemic similarity information. • Multivariate Fuzzy Trend Matching (MFTM) is developed to predict the long-term trend. • Informer is adept at extracting intricate patterns from high-frequency data. Multi-step forecasting is a critical process in various fields, such as disaster warning and financial analysis. Nevertheless, achieving precise multi-step forecasting is challenging due to the intricate nature of the factors influencing the time series, most of which are highly nonlinear and nonstationary. In this paper, a multi-step forecasting model named MFTM-Informer, employing a multiple input multiple output strategy for multivariate trend matching is proposed. The dependent variable and the influencing factors are initially decomposed using multivariate variational modal decomposition to minimize noise. Afterwards, the decomposed data are reconstructed into multivariate trends and fluctuations using sample entropy, enabling the development of tailored forecasting strategies based on data characteristics. A multivariate trend is predicted using an enhanced pattern matching model, while the high-frequency fluctuation is modelled using Informer. Finally, the outcomes are combined to generate multi-step predictions. To validate the performance of the proposed model, we observed its performance on three real-world datasets, including Brent crude oil prices, European Union Allowance future prices, and Standard & Poor's 500 index. Results indicate that the model surpasses all the benchmark models in terms of multiple evaluation metrics and forecast ranges, highlighting its effectiveness and robustness in multi-step forecasting. [ABSTRACT FROM AUTHOR]

Feature selection addresses the dimensionality-reduction problem by determining a subset of available features, which facilitates the construction of effective prediction models. However, with regression tasks where the target variable is continuous and imbalanced, ordinary feature-selection techniques cannot be simply used without adjustment. This paper proposes SerEnet, a novel method of sparse feature selection in imbalanced regression, which explores both feature selection and estimation simultaneously by minimizing the Squared Error-Relevance with respect to a cutoff t (S E R t), subject to a sparse penalty. Specifically, S E R t considers the performance of target variables with relevance greater than t in the target-variable domain and emphasizes the error of rare values. Moreover, SerEnet can effectively identify features that contribute significantly to rare cases, thereby reducing the dominant influence of common instances on feature selection, and improving prediction performance for both rare values and overall data. Experimental results on simulated and real datasets show that SerEnet outperformed several algorithms in terms of prediction performance on continuous-imbalanced data. [ABSTRACT FROM AUTHOR]

Ensemble learning (EL) boosts model prediction performance across various domains through two main steps: generating individual classifiers (ICs) and combining them. Creating accurate and diverse ICs is crucial for a strong ensemble, while selecting the best ICs, known as ensemble selection (ES), is critical yet challenging due to the accuracy-diversity trade-off and the lack of agreed-upon diversity metrics. This paper introduces an EL strategy that uses multi-objective feature selection (MOFS) and a feature relevance-guided selection to tackle these challenges. Our approach uses a hybrid MOFS algorithm to produce accurate and diverse ICs, and then it employs a novel knowledge-based feature-relevance-guided metric for precise diversity assessment during ES. The ES issue is cast as an optimization problem, aiming to maximize both diversity and accuracy, and an efficient ES algorithm is developed to select optimal ICs. Extensive tests on public datasets and a real-world prediction task demonstrate the effectiveness of our method, especially in achieving high accuracy. [ABSTRACT FROM AUTHOR]

• Existing time series forecasting models make it difficult to extract multiple common features in the data, such as sudden irregular fluctuation trends, trend features with large fluctuation amplitudes, and factors such as long-term dependencies in the time series. To solve this problem, a heterogeneous model NeA3L that considers the common features of time series is proposed. • The proposed model realizes multiple independent parallel feature extraction modules by combining multi-task learning, which effectively captures the common features of multivariate time series the long-term dependency features in the time series and enhances the model's ability to extract common features of time series. The model realizes the feature fusion of multivariate time series by combining the attention mechanism. By designing a deep iterative LSTM network, the model handles sudden irregular fluctuation regular features. • Comparing with six state-of-the-art time series forecasting models and three traditional forecasting models on four different domain public datasets, the model outperforms nine benchmark models in all forecasting indexes, indicating that the model in this paper has the ability of universal applicability in the field of multivariate time series forecasting, which is of great significance for optimizing intelligent management and decision-making. Multivariate time series data contain a variety of common features that are difficult to extract, among which the sudden irregular fluctuation trend, the trend feature of large fluctuation amplitude, and the long-term dependency relationship in the time series have an important impact on the accuracy of the prediction model. To predict non-stationary trends in large-scale data accurately using the common characteristics of multivariate time series. A novel generalised forecasting model NeA3L based on common features of time series is developed. The NeA3L model utilizes multiple independent parallel feature extraction modules to obtain the common features of multivariate time series. It utilizes the three-layer iterative structure to deal with sudden irregular fluctuation patterns. NeA3L optimizes the network structure to realize the heterogeneous codec structure. It applies the attention mechanism between the encoder and the decoder to accomplish the multivariate prediction of the multivariate time series, which has good stability for predicting various types of multivariate data. A comparison of the NeA3L model with nine current time series prediction methods on four publicly available datasets shows that the NeA3L model outperforms the current methods in various evaluation metrics. The RMSE is improved by 2.3 %, 26.5 %, 28.9 %, and 20.84 % on average, respectively. The NeA3L model can be universally applicable to the field of multivariate time series prediction, which is important for optimizing the intelligent management and decision-making. [ABSTRACT FROM AUTHOR]

In engineering problem, there exists many mixed datasets composed of multi-type data, including data without noise, data with homoscedastic noise (known noise variance), and data with heteroscedastic noise (unknown noise variance). To construct accurate predictive models, it is essential to fully utilize the information provided by the diverse dataset, particularly when confronted with limitations in training samples. Motivated by this, this paper introduces a novel transfer learning surrogate modeling approach to fuse multi-type data for prediction purpose. Numerical cases and two engineering applications validate the accuracy and robustness of the proposed model. Comparative analyses reveal superior predictive accuracy and robustness compared to other models. Additionally, by varying the levels of homoscedastic and heteroscedastic noise, the impact of noise on the proposed model is studied. Results indicate that this model has superior applicability and stability compared to other models. The proposed approach is capable of fusing multi-type data and adapting to different levels of noise, making it a promising approach for engineering application. [ABSTRACT FROM AUTHOR]

The dynamic reservoir of the randomly generated Echo State Network (ESN) contains numerous redundant neurons, resulting in collinearity in the high-dimensional state space matrix. This collinearity impacts the prediction performance of the network. In order to address this issue, this paper introduces a self-organizing ESN structure optimization model that is based on reinforcement learning, called SR-ESN. The SR-ESN model employs pruning methods to reconstruct the reservoir using contribution and decision mechanisms. To mitigate potential instability caused by high coupling among neurons in a single reservoir, the concept of ensemble learning is applied to create multiple initial reservoir pools, thereby enhancing screening diversity. Simultaneously, the model utilizes reinforcement learning's decision mechanism to identify effective neurons. Neurons with low contribution are pruned, while those with high contribution are retained for self-organizing reconstruction. This optimization of the network structure enhances its prediction performance. Based on both artificial and real datasets, the proposed SR-ESN model demonstrates superior prediction performance with minimal structural complexity compared to other prediction models. [ABSTRACT FROM AUTHOR]

• A novel dynamic social interaction modeling mechanism based on structure entropy minimization. • A separate scene node to model the motion patterns and behavioral preferences. • A multi-time-scale motion tendency modeling module. • State-of-the-art results on the ETH, UCY and SDD datasets. Trajectory is an important basis for reflecting the behavior of moving agents and can be used for various applications. Autonomous systems navigating in complex scenes should have the ability to predict the future locations of surrounding agents and avoid collisions. Agents in a scene interact with each other constantly, which is the greatest challenge of trajectory prediction. However, existing methods have problems with insufficient, superfluous or inaccurate interactions, whether they are distance-, attention- or dense graph-based interactive mechanisms. Moreover, the dynamic complexity and validity of social interactions cannot be quantified. In this paper, we propose a dynamic social interaction modeling mechanism based on structure entropy minimization. Specifically, structure entropy minimization provides a principle for detecting and quantifying the natural or true interactions between agents. Additionally, considering that most agents under the same scenario often have similar behavioral preferences, we introduce scene features to model the interscene variance and intrascene consistency of motion patterns. Finally, through multi-time-scale motion tendency modeling, we can simultaneously model short-term and long-term intentions to alleviate accumulated errors for pedestrians avoiding collisions. We evaluate our proposed method on three trajectory datasets, and the experiments and comparisons demonstrate that our method outperforms comparative state-of-the-art methods while demonstrating the capacity to model social interaction and motion patterns. [ABSTRACT FROM AUTHOR]

• A novel hierarchical graph representation learning model for drug-target binding affinity prediction. • Represents the drug-target binding affinity data as a hierarchical graph. • Hierarchically integrates coarse- and fine-level information in a coarse-to-fine manner. Computationally predicting drug-target binding affinity (DTA) has attracted increasing attention due to its benefit for accelerating drug discovery. Currently, numerous deep learning-based prediction models have been proposed, often with a biencoder architecture that commonly focuses on how to extract expressive representations for drugs and targets but overlooks modeling explicit drug-target interactions. However, known DTA can provide underlying knowledge about how the drugs interact with targets that is beneficial for predictive accuracy. In this paper, we propose a novel hierarchical graph representation learning model for DTA prediction, named HGRL-DTA. The main contribution of our model is to establish a hierarchical graph learning architecture to integrate the coarse- and fine-level information from an affinity graph and drug/target molecule graphs, respectively, in a well-designed coarse-to-fine manner. In addition, we design a similarity-based representation inference method to infer coarse-level information when it is unavailable for new drugs or targets under the cold start scenario. Comprehensive experimental results under four scenarios across two benchmark datasets indicate that HGRL-DTA outperforms the state-of-the-art models in almost all cases. [ABSTRACT FROM AUTHOR]

Semi-supervised partial label learning is an emerging weakly supervised learning paradigm dealing with partially labeled data and unlabeled data simultaneously. The supervision information is weak and even missing for training instances. Most existing researches simply propagate labels to unlabeled data and focus on inducing a weighted classifier model, ignoring the importance of acquiring reliable label confidences for training instances. In this paper, by label set assignment, the valid label supervision information can be preserved and propagated to unlabeled instances to a greater extent. Then, the training instances are projected into a lower-dimensional feature space. Reliable label confidence can be obtained by maximizing the dependence between the feature description and the associated label information. After that, the predictive model can be induced by preserving the intrinsic manifold structure in both feature space and label space. Accordingly, a novel semi-supervised partial label learning approach via dependence-maximized label set assignment (Dlsa) is proposed, where an accurate and robust model can be induced based on reliable label confidences obtained by label set assignment and dependence-maximized dimensionality reduction. Extensive experiments on five real-world data sets validate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• A hierarchical clustering-based manifold transfer learning (CMTL) method is developed for dynamic multi-objective optimization problems. • A multi-source transfer learning (MSTL) method is designed to fully exploit all historical knowledge, which can excavate valuable knowledge from the historical environment to predict a high-quality initial population for the new environment. • By incorporating the proposed CMTL and MSTL transfer learning methods, a new DMOEA, called MSTL-DMOEA, is proposed for dynamic optimization. • The superiority of the proposed MSTL-DMOEA over existing prediction-based and transfer-based methods is demonstrated. Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs can effectively generate a good initial population for the new environment. However, most of them only transfer non-dominated solutions from the previous one or two environments, which cannot fully exploit all historical information and may easily induce negative transfer as only limited knowledge is available. To address this problem, this paper presents a multiple source transfer learning method for DMOEA, called MSTL-DMOEA, which runs two transfer learning procedures to fully exploit the historical information from all previous environments. First, to select some representative solutions for knowledge transfer, one clustering-based manifold transfer learning is run to cluster non-dominated solutions of the last environment to obtain their centroids, which are then fed into the manifold transfer learning model to predict the corresponding centroids for the new environment. After that, multiple source transfer learning is further run by using multisource TrAdaboost, which can fully exploit information from the above centroids in new environment and old centroids from all previous environments, aiming to construct a more accurate prediction model. This way, MSTL-DMOEA can predict an initial population with better quality for the new environment. The experimental results also validate the superiority of MSTL-DMOEA over several competitive state-of-the-art DMOEAs in solving various kinds of DMOPs. [ABSTRACT FROM AUTHOR]

Abstract In machine learning tasks, it is essential for a data set to be partitioned into a training set and a test set in a specific ratio. In this context, the training set is used for learning a model for making predictions on new instances, whereas the test set is used for evaluating the prediction accuracy of a model on new instances. In the context of human learning, a training set can be viewed as learning material that covers knowledge, whereas a test set can be viewed as an exam paper that provides questions for students to answer. In practice, data partitioning has typically been done by randomly selecting 70% instances for training and the rest for testing. In this paper, we argue that random data partitioning is likely to result in the sample representativeness issue, i.e., training and test instances show very dissimilar characteristics leading to the case similar to testing students on material that was not taught. To address the above issue, we propose a subclass-based semi-random data partitioning approach. The experimental results show that the proposed data partitioning approach leads to significant advances in learning performance due to the improvement of sample representativeness. [ABSTRACT FROM AUTHOR]

Highlights • The algorithm of local tangent space arrangement is applied to the field of software defect distribution prediction. LTSA is used to reduce the size of input data space and reduce the training and testing time of SVM. In addition, LTSA can also reduce the noise interference of data sets and improve the mapping performance, which directly achieves the goal of improving software defect prediction index. • The data redundancy problem is solved. Firstly, we construct high-dimensional feature space of software defect data set. Then, in order to solve the data redundancy problem of software defect data set, we use local tangent space algorithm which is a non-linear manifold learning algorithm to extract the hidden low-dimensional submanifolds. • Selecting parameter adaptively. The grid search algorithm selects the parameters of the dimension and the neighborhood, and realizes the selection of the local neighborhood parameters in the high-dimensional space adaptively to guarantee the parameter optimality. Abstract With the continued growth of interoperable software developed for Internet of Things (IoT), there is a growing demand to predict software defect at various testing and operational phases. This paper solves the software defect prediction problem by proposing a novel model with the help of a local tangent space alignment support vector machine (LTSA-SVM) algorithm. The model employs the SVM algorithm as the basic classifier of software defect distribution prediction model. Then, the model parameters are optimized by combining a grid search method and ten-fold cross validation. In the traditional dimensionality reduction algorithms, data loss caused by the poor attributes of data nonlinearity reduces the accuracy of SVM. Aiming at this problem, this paper uses a LTSA algorithm to extract the intrinsic structure of low-dimensional data and performs effective dimension reduction. The SVM algorithm is trained by the reduced dimension data. Finally, the feasibility of the prediction model is verified. Compared with the single SVM and the LLE-SVM prediction algorithm, the prediction model in this paper improves the prediction accuracy and F-measure by 1–4%. [ABSTRACT FROM AUTHOR]

In view of the high propagation space and the complex networking of rumors, this paper proposes a group behavior prediction model based on sparse representation and interaction of complex messages. Firstly, to solve the difficulty in model training caused by the high dimension and complexity of rumor space, sparse representation is considered as the theoretical basis to construct sparse vectors for user node features, and to construct the node feature prediction submodel. Secondly, aiming at the dynamic interactive behavior among complex messages in the rumor space, the driving force of complex messages is quantified with the evolutionary game, the dynamic rumor propagation network is reconstructed, and the structure attribute prediction submodel is constructed. Finally, considering the advantages of model fusion in improving the generalization ability of the single model, the node feature prediction submodel—Submodel Based on SRC and the structure attribute prediction submodel—Submodel Based on Node2Vec are fused. Meanwhile, a dynamic group behavior prediction model under the influence of complex messages is constructed for the time-sensitive nature of rumor propagation. The experimental results show that the model not only effectively explores the interaction between complex messages but also accurately predicts the group behavior and depicts the rules of rumor propagation. [ABSTRACT FROM AUTHOR]

• Model a fusion user embedding with considering local and global interest semantics. • Explain social relations by learning neighbors' bootable and transitive influences. • Propose a hybrid personalized and neighbor attention model for link prediction. Friend link prediction is an important research problem in recommender systems. Existing network embedding and knowledge embedding methods mainly consider the structural relationships of entities, ignoring the explanatory role of text contents. In this paper, we present an explainable friend link recommendation method that leverages direct and indirect similarity of user pairs via fusion embedding of heterogeneous context information. In social networks, while considering user content interests, first, a fusion user embedding method was developed by incorporating external knowledge semantics. Second, for a user pair, we calculate their direct similar relationship using fusion user embeddings. Additionally, based on intermediate neighbors, we compute their indirect similar relationship by using an attention mechanism, which explains neighbors' bootable and transitive influences for learning the social relationship of user pairs. Then, a hybrid personalized and neighbor attention model for friend link prediction was proposed by considering direct and indirect factors. Finally, experiments were conducted on the Sina Weibo datasets, which indicates that the proposed method effectively predicts the friends of users and provides a good interpretation for link prediction recommendation results. [ABSTRACT FROM AUTHOR]

• Model a fusion user embedding with considering local and global interest semantics. • Explain social relations by learning neighbors' bootable and transitive influences. • Propose a hybrid personalized and neighbor attention model for link prediction. Friend link prediction is an important research problem in recommender systems. Existing network embedding and knowledge embedding methods mainly consider the structural relationships of entities, ignoring the explanatory role of text contents. In this paper, we present an explainable friend link recommendation method that leverages direct and indirect similarity of user pairs via fusion embedding of heterogeneous context information. In social networks, while considering user content interests, first, a fusion user embedding method was developed by incorporating external knowledge semantics. Second, for a user pair, we calculate their direct similar relationship using fusion user embeddings. Additionally, based on intermediate neighbors, we compute their indirect similar relationship by using an attention mechanism, which explains neighbors' bootable and transitive influences for learning the social relationship of user pairs. Then, a hybrid personalized and neighbor attention model for friend link prediction was proposed by considering direct and indirect factors. Finally, experiments were conducted on the Sina Weibo datasets, which indicates that the proposed method effectively predicts the friends of users and provides a good interpretation for link prediction recommendation results. [ABSTRACT FROM AUTHOR]

Feature importance and interaction are among the main issues in explainable artificial intelligence or interpretable machine learning. To measure feature importance and interaction, several methods, such as H-statistic and partial dependency, have been proposed. However, it is difficult to understand the practical implications of importance and interaction. In this paper, a new method for measuring feature importance and interaction is proposed. For the classification model, we observed correctly predicted cases in a predictive model and grouped them according to the characteristics of the cases. We derived a method for feature importance and interaction from group information. For the regression model, we grouped cases according to the change in the size of the prediction error. The proposed method supports the same rationale for feature importance and interaction. It also supports the decomposition of feature importance to feature power and feature interactions. To implement the proposed method, three visualization tools, including a feature interaction graph, are implemented. Through the proposed work, we can better understand the working mechanism of a predictive model. [ABSTRACT FROM AUTHOR]

In predictive modeling, quantifying prediction uncertainty is crucial for reliable decision-making. Traditional conformal inference methods provide marginally valid predictive regions but often produce non-adaptive intervals when naively applied to regression, potentially biasing applications. Recent advances using quantile regressors or conditional density estimators improve adaptability but are typically tied to specific prediction models, limiting their ability to quantify uncertainty around arbitrary models. Similarly, methods based on partitioning the feature space adopt sub-optimal strategies, failing to consistently measure predictive uncertainty across the feature space, especially in adversarial examples. This paper introduces a model-agnostic family of methods to calibrate prediction intervals for regression with local coverage guarantees. By leveraging regression trees and Random Forests, our approach constructs data-adaptive partitions of the feature space to approximate conditional coverage, enhancing the accuracy and scalability of prediction intervals. Our methods outperform established benchmarks on simulated and real-world datasets. They are implemented in the Python package clover, which integrates seamlessly with the scikit-learn interface for practical application. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) prevail in the solution of computationally expensive optimization problems. However, with the growth of problem scale and complexity, the high-dimensional problem space greatly restricts their effectiveness and applicability. This paper proposes a Q-learning driven competitive surrogate-assisted evolutionary optimizer with multiple oriented mutation operators (CSEO-MOMO) to improve the prediction effectiveness and accuracy of SAEAs in different scenarios of optimization problems. CSEO-MOMO constructs isomorphic models with different complexities during iteration. The Q-learning method is employed to pick the proper model for prediction based on the state feedback of the iterative population. To explore the search space more comprehensively and increase the probability of finding a better solution, two competitive sub-populations coevolve with personalized mutation operators, of which different oriented optimal tractive base vectors are nominated to guide the mutation direction. In addition, a tailored infilling strategy concerning both the performance of the iterative individuals and their spatial distribution is used to winnow out the individuals with great potential for real evaluation. A suit of high-dimensional CEC benchmarks of different properties and an openly shared topology optimization problem are used for the effectiveness and efficiency verification of CSEO-MOMO. The experimental results reveal that CSEO-MOMO trumps seven excellent SAEAs in solving high-dimensional complex problems and possesses better accuracy and robustness under various fitness landscape scenarios. (The relevant MATLAB code of CSEO-MOMO is publicly available in the first author's GitHub repository: https://github.com/qinna-zhu/CSEO-MOMO.) [ABSTRACT FROM AUTHOR]

Social networking services allow users to adopt and spread information via diffusion actions, e.g., share, retweet, and reply. Real applications such as viral marketing and trending topic detection rely on information diffusion. Given past items with diffusion records on a social network, this paper aims at forecasting who will participate in the diffusion of a new item c (we use hashtags in the paper) with its k earliest adopters, without using content and profile information, i.e., finding which users will adopt c in the future. We define the Diffusion Participation Forecasting (DPF) problem, which is challenging since all users except for early adopters can be the candidates, comparing to existing studies that predict which one-layer followers will adopt a new hashtag given past diffusion observations with content and profile info. To solve the DFP problem, we propose an Adoption-based Participation Ranking (APR) model, which aims to rank the actual participants in reality at higher positions. The first is to estimate the adoption probability of a new hashtag for each user while the second is a random walk-based model that incorporates nodes with higher adoption probability values and early adopters to generate the forecasted participants. Experiments conducted on Twitter exhibit that our model can significantly outperform several competing methods in terms of Precision and Recall. Moreover, we demonstrate that an accurate DPF can be applied for effective targeted marketing using influence maximization and boosting the accuracy of popularity prediction in social media. [ABSTRACT FROM AUTHOR]

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) aims to recognize unseen classes during training. Transductive methods have advanced in ZSL, however, often rely on pseudo labels based on confidence scores, leading to semantic misalignment between unseen-class image features and corresponding class semantic descriptions due to noisy pseudo labels. In this paper, we introduce a novel Consistency-Guided Pseudo-Labeling (CGPL) to generate high-quality pseudo labels, achieving robust mapping from visual to semantic space for unseen classes. CGPL incorporates a large-scale vision-language model as a collaborator with the ZSL model to generate high-quality pseudo-labels. Then, pseudo-labeled samples with consistent prediction of two models are added to the training set, to learn the visual-to-semantic mapping for unseen classes. Furthermore, we design a quasi-classification loss based on reconstructed unseen prototypes to learn accurate visual-semantic mapping. Consequently, CGPL is further encouraged to obtain higher-quality pseudo labels, and progressively learn the precise visual-semantic mapping for unseen classes throughout the iterative process. Our extensive experimental results across four benchmark datasets highlight the superior performance of CGPL in both CZSL and GZSL settings. [ABSTRACT FROM AUTHOR]

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm. [ABSTRACT FROM AUTHOR]

Ordinal regression (OR) is a paradigm which learns a prediction model on the data with ordered classes. Despite much progress in OR, the existing OR works learn the classifier from only one view and the multi-view learning in OR has not been considered. What is more, there may exist uncertain information in the multi-view OR data. In this paper, we put forward a novel approach, called multi-view support vector ordinal regression with uncertain data (MORU), which can improve the OR classifier by incorporating the multi-view information and handling the data uncertainty. In our method, a series of parallel hyperplanes are applied to separate the multi-view ordered data, and the uncertain information is considered in the input data. Then, we adopt a heuristic framework to solve the OR learning problem. Experimental results have illustrated that our method obtains superior performance to the existing OR techniques. [ABSTRACT FROM AUTHOR]

• Propose a feasible paradigm for using multidomain data in big data analysis. • Apply multigranularity analysis to small-scale low-dimensional data sets. • Transform prediction into a classification problem using deep forest computation. When addressing analysis and prediction problems in a specific domain based on big data processing, the following problems often arise: only relationships between features in the domain itself are considered, and existing methods are not effective for training models on small sample data sets. The traditional approach usually obtains the relationships between single-domain features. Analysis and forecasting in the problem domain alone can quickly achieve good accuracy, but due to the limitations of the analysis domain, it becomes increasingly difficult to further improve the prediction accuracy. This paper proposes a novel data analysis approach compatible with small sample sets called multidomain data depth analysis (MODE). In contrast to traditional approaches, MODE emphasizes multidomain data and considers the relationships among feature domains in the original data. The features in each domain are orthogonally extracted, and feature dimensions are expanded in accordance with the characteristics of small data sets. A better prediction model can be obtained by using the expanded and strengthened features. We apply this approach to real big data from the field of sociology to predict annual income based on census data in experiments. The experimental results show that MODE offers a better prediction effect based on small multidomain samples. [ABSTRACT FROM AUTHOR]

Time-series data are widespread in real-world industrial scenarios. To recover and infer missing information in real-world applications, the problem of time-series prediction has been widely studied as a classical research topic in data mining. Deep learning architectures have been viewed as next-generation time-series prediction models. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks. In this study, we prospectively examine the problem of time-series prediction adversarial attacks and propose an attack strategy for generating an adversarial time series by adding malicious perturbations to the original time series to deteriorate the performance of time-series prediction models. Specifically, a perturbation-based adversarial example generation algorithm is proposed using the gradient information of the prediction model. In practice, unlike the imperceptibility to humans in the field of image processing, time-series data are more sensitive to abnormal perturbations and there are more stringent requirements regarding the amount of perturbations. To address this challenge, we craft an adversarial time series based on the importance measurement to slightly perturb the original data. Based on comprehensive experiments conducted on real-world time-series datasets, we verify that the proposed adversarial attack methods not only effectively fool the target time-series prediction model LSTNet, they also attack state-of-the-art CNN-, RNN-, and MHANET-based models. Meanwhile, the results show that the proposed methods achieve a good transferability. That is, the adversarial examples generated for a specific prediction model can significantly affect the performance of the other methods. Moreover, through a comparison with existing adversarial attack approaches, we can see that much smaller perturbations are sufficient for the proposed importance-measurement based adversarial attack method. The methods described in this paper are significant in understanding the impact of adversarial attacks on a time-series prediction and promoting the robustness of such prediction technologies. [ABSTRACT FROM AUTHOR]

[Display omitted] Data selection, which includes feature and instance selection, is often an important step in building prediction systems. Genetic algorithms (GA) frequently allow finding better solutions than classical methods in many areas. This is also true for the instance selection task. The main difficulties and challenges in GA-based instance selection are high computational complexity and decreasing performance with the dataset size growth. This is caused by the fact that each instance is encoded in one chromosome position. Hence bigger datasets result in longer chromosomes. The main contribution of this paper addresses the above problems in a three-step process. In the first step the dataset is divided into several consistent regions by fuzzy clustering. Then GA-based instance selection is performed independently within each cluster. Finally ensemble voting provides seamless aggregation of the partial results from the overlapping clusters. This improves dataset exploitation by more localized search and also takes the advantage of ensemble methods. This method significantly improves the predictive model performance and data reduction in comparison to instance selection performed on the whole training dataset. [ABSTRACT FROM AUTHOR]

• A novel semi-supervised self-organizing fuzzy inference system (S3OFIS+) is introduced for data stream classification. • A novel parameter-free pseudo-labelling strategy is proposed for S3OFIS+ to effectively self-train from unlabelled data. • The proposed S3OFIS+ can self-learn from unlabelled data streams chunk-by-chunk in a single-pass manner with minimum human input. • The proposed S3OFIS+ can provide users high interpretability and explainability thanks to its prototype-based nature. As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To overcome the labelling bottleneck and construct a stronger classification model, a novel semi-supervised EIS is proposed in this paper. After being primed with a small amount of labelled data, the proposed method is capable of continuously self-developing its system structure and self-updating the meta-parameters from unlabelled data streams chunk-by-chunk in a non-iterative, exploratory manner by exploiting a novel pseudo-labelling strategy. Thanks to its transparent prototype-based structure and human-understandable reasoning process, the proposed method can provide users high explainability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the development of online social networks, user behavior prediction based on the data collected from these social networks has attracted increasing attention. In heterogeneous social networks, a node usually has several heterogeneous attributes to describe itself from different angles. However, most existing methods only utilize an attribute of each node and neglect other heterogeneous attributes. Therefore, this paper proposes a new user heterogeneous information embedding method, called user heterogeneous information embedding (UHIE). This method utilizes the attention mechanism to aggregate the heterogeneous attribute information of each neighbor to obtain their low-dimension representation. Then, the graph neural network is employed to aggregate the multi-relational information from neighbors to obtain the low-dimension representation of nodes. Furthermore, a new soft thresholding method is proposed to eliminate the unimportant information, called multi-head self-attention soft thresholding (MSST), which employs the multi-head self-attention mechanism to calculate an importance threshold for each feature. Based on UHIE and MSST, a new user behavior prediction model is proposed, called Heterogeneous Residual Self-Attention Shrinkage Network (HRSN). This model utilizes UHIE to aggregate heterogeneous information including all heterogeneous attribute information of nodes, and employs MSST to eliminate unimportant information. The experimental results on three real-world datasets show the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Due to small-quantity and often imbalance of labeled samples, it is challenging to establish a robust and accurate prediction model through data-driven methods. To deal with the small dataset problem, new virtual samples may be generated via virtual sample generation (VSG) methods based on the trend of the original small raw dataset, thereby improving modeling performance. Effective VSG is desirable, but also challenging. Conventional VSG usually assumes that the raw sample set contains only a single operating mode. Taking multi-mode into account will improve the VSG based modeling performance since actual processes are often multi-mode. To this end, an information expansion function considering sample density and amount (IEDA) is first developed to expand the domain range of the attributes in this paper. Then, virtual samples under the multiple operating mode condition are generated by proposing a Gaussian mixture model based virtual sample generation (GMMVSG) method. Applications of GMMVSG on Tennessee Eastman benchmark process and an industrial hydrocracking process show significant improvement of modeling and predictions over other conventional VSG methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A time-varying consensus formation scheme for mobile robots with switching topology is built. • The robots' input can be simultaneously got by the NDMPC-based protocol. • The constraints of the formation system can be considered. In this paper, for controlling the time-varying consensus formation (TVCF) of multiply nonholonomic robots under switching information transforming topology, a generalized consensus formation system, which is controlled by a neural-dynamic-optimized distributed model predictive control (NDMPC) based consensus protocol strategy, is developed. The system consists of an auxiliary consensus maneuvering subsystem and a formation tracking subsystem. Through simultaneously stabilizing the generalized errors consisted of these two subsystems under the switching topologies, the consensus object is achieved. Within each sampling time, the NDMPC method can formulate and solve a constrained quadratic programming (QP) problem with time-varying desired formation pattern and get the optimal inputs for each robot in a distributed manner. The constraints of the system, as well as the switching structure brought by the changing topologies, can be tackled by utilizing the proposed method. In the end, numerical examples verify the effectiveness of the proposed formation control method. [ABSTRACT FROM AUTHOR]

High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to better predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network (LSTM) and wavelet, is introduced in this paper. In an empirical study with practical passenger flow data of Dongzhimen Station in the Beijing Subway system, the hybrid model exhibited more effective performance in terms of prediction accuracy than the existing algorithms, e.g., autoregressive integrated moving average (ARIMA), nonlinear regression (NAR), and traditional LSTM model. The study illustrates that our newly adopted model is a promising approach for predicting high-precision short-term inbound passenger flow. [ABSTRACT FROM AUTHOR]

This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity by coupling the MSGP and multiple regression LASSO methods. The new model uses average mutual information to identify the optimum lags, and root mean-square technique to minimize forecasting error. Based on Nash-Sutcliffe efficiency and bias-corrected Akaike information criterion, MSGP-LASSO is superior to GP, multigene GP, MSGP, and hybrid MSGP-least-square models. It is explicit and promising for real-life applications. [ABSTRACT FROM AUTHOR]

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided. [ABSTRACT FROM AUTHOR]

• Introduction of the maximum profit measure for evaluating uplift models. • Presentation of a real-world case study on uplift modeling for customer retention. • Customer churn uplift models are found to outperform customer churn prediction models. • Uplift modeling is concluded to be a superior tool for customer churn and retention management, increasing the returns on marketing investment. Uplift modeling has received increasing interest in both the business analytics research community and the industry as an improved paradigm for predictive analytics for data-driven operational decision-making. The literature, however, does not provide conclusive empirical evidence that uplift modeling outperforms predictive modeling. Case studies that directly compare both approaches are lacking, and the performance of predictive models and uplift models as reported in various experimental studies cannot be compared indirectly since different evaluation measures are used to assess their performance. Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift (MPU) measure that allows assessing the performance in terms of the maximum potential profit that can be achieved by adopting an uplift model. This measure, developed for evaluating customer churn uplift models, extends the maximum profit measure for evaluating customer churn prediction models. While introducing the MPU measure, we describe the generally applicable liftup curve and liftup measure for evaluating uplift models as counterparts of the lift curve and lift measure that are broadly used to evaluate predictive models. These measures are subsequently applied to assess and compare the performance of customer churn prediction and uplift models in a case study that applies uplift modeling to customer retention in the financial industry. We observe that uplift models outperform predictive models and lead to improved profitability of retention campaigns. [ABSTRACT FROM AUTHOR]

• A gradient-based influence propagation strategy to get the attacking route. • Input perturbations are mainly propagated by this route. • Use gradients to evaluate samples' adversarial hardness. Deep neural networks (DNNs) are vulnerable to adversarial examples which are generated by inputs with imperceptible perturbations. Understanding adversarial robustness of DNNs has become an important issue, which would for certain result in better practical deep learning applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of critical attacking route, which is computed by a gradient-based influence propagation strategy. Similar to rumor spreading in social networks, we believe that adversarial noises are amplified and propagated through the critical attacking route. By exploiting neurons' influences layer by layer, we compose the critical attacking route with neurons that make the highest contributions towards model decision. In this paper, we first draw the close connection between adversarial robustness and critical attacking route, as the route makes the most non-trivial contributions to model predictions in the adversarial setting. By constraining the propagation process and node behaviors on this route, we could weaken the noise propagation and improve model robustness. Also, we find that critical attacking neurons are useful to evaluate sample adversarial hardness that images with higher stimulus are easier to be perturbed into adversarial examples. [ABSTRACT FROM AUTHOR]

The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Partial Multi-Label learning (PML) aims to learn from training data where each example is associated with a set of candidate labels, among which only a subset of them is correct. The major challenge of PML lies in that the training procedure is prone to be misled by the label noise. To address this problem, nearly all existing PML methods focus on solely label disambiguation, i.e., dislodging the noisy labels from the candidate label set and then utilizing the remaining credible labels for model induction. However, these remaining "credible" labels may be incorrectly identified, which thereby would have a huge adverse impact on the subsequent model induction. In this paper, in contrary to the above label disambiguation strategy, we propose a simple yet effective N oisy l A bel T olerated p A rtial multi-label L earning (NATAL) method, where the labeling information is considered to be precise while the feature information is assumed to be missing. Using our proposed method, the task of PML can be re-interpreted as a Feature Completion problem, and the desired prediction model can be directly induced from the completed feature together with all candidate labels. Extensive experimental results on various data sets clearly demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment transition dynamics into a predictive model. The transition dynamics describes the mapping from one state to another, conditioned on an action. If this model is accurate enough, the predictive model is sufficient to train agents for optimal behavior in real environments. This paper presents the Dreaming Variational Autoencoder (DVAE) for safely learning good policies with a significantly lower risk of catastrophes occurring during training. The algorithm combines variational autoencoders, risk-directed exploration, and curiosity to train deep-q networks inside "dream" states. We introduce a novel environment, ASRS-Lab, for research in the safe learning of autonomous vehicles in grid-based warehousing. The work shows that the proposed algorithm has better sample efficiency with similar performance to novel model-free deep reinforcement learning algorithms while maintaining safety during training. [ABSTRACT FROM AUTHOR]

In order to show high dynamic range (HDR) images by traditional displays, various tone-mapping operators have been designed to convert HDR images into low dynamic range (LDR) images recently. However, how to estimate the visual quality of LDR images effectively is still challenging. In this paper, we propose a novel blind quality assessment method for tone-mapped images with the consideration of naturalness and the perceptual characteristics of human visual system (HVS). First, we design parametric models that describe characteristics of chromatic information in tone-mapped images and extract quality-aware features based on global statistics model to characterize the naturalness of tone-mapped images. Second, motivated by perceptual characteristics that the HVS is highly adaptive to the image texture, we employ local texture features to capture the quality degradation of tone-mapped images. Support vector regression (SVR) is used to train the quality prediction model from features to human ratings. Experimental results indicate that the proposed metric can get better performance in predicting the visual quality of tone-mapped images than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• We propose a deep rule ensemble creation approach. • It is driven by iterative expansion of the feature space. • It involves multiple ways of heuristic creation of diversity. • We compare the proposed approach with existing rule learning and ensemble methods. • The proposed approach achieves significant advances in classification accuracy. Rule learning approaches, which essentially aim to gerenate a decision tree or a set of "if-then" rules, have been popularly used in practice for automatically building rule-based models for prediction tasks, e.g., classification and regression. The key strength of rule-based models is their ability to interpret how an output is obtained given an input, in comparison with models trained by other machine learning approaches, e.g., neural networks. Moreover, ensemble learning approaches have been adopted as a popular way for advancing the performance of rule-based prediction through producing multiple rule-based models with diversity. Traditional approaches of ensemble learning are typically designed to train a single ensemble. In recent years, there have been some studies on creation of multiple ensembles towards increasing the diversity among rule-based models and the depth of ensemble learning. In this paper, we propose a feature expansion driven approach for automatic creation of deep rule ensembles, i.e., the dimensionality of the feature space is increased at each iteration by adding features newly created at the previous iteration. The proposed approach is compared with more recent approaches of rule learning and ensemble creation. The experimental results show that the proposed approach achieves improved performance on various data sets. [ABSTRACT FROM AUTHOR]