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Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules. [ABSTRACT FROM AUTHOR]

Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These marketplaces in turn deploy review fraud detection systems so that the integrity of reviews is preserved. A well-known problem with review fraud detection systems is their underlying assumption that the majority of reviews are honest-this assumption leads to a vulnerability where an attacker can try to generate many fake reviews of a product. In this article, we consider the case where a company wishes to fraudulently promote its product through fake reviews and propose the Sockpuppet-based Targeted Attack on Reviewing Systems (STARS for short). STARS enables an attacker to enter fake reviews for a product from multiple, apparently independent, sockpuppet accounts. We show that the STARS attack enables companies to successfully promote their product against seven recent, well-known review fraud detectors on four datasets (Amazon, Epinions, and the BitcoinAlpha and OTC exchanges) by significant margins. To protect against the STARS attack, we propose a new fraud detection algorithm called RTV. RTV introduces a new class of users (called trusted users) and also considers reviews left by verified users which were not considered in existing review fraud detectors. We show that RTV significantly mitigates the impact of the STARS attack across the four datasets listed above. [ABSTRACT FROM AUTHOR]

We advocate for and present TourSense, a framework for tourist identification and preference analytics using city-scale transport data (bus, subway, etc.). Our work is motivated by the observed limitations of utilizing traditional data sources (e.g., social media data and survey data) that commonly suffer from the limited coverage of tourist population and unpredictable information delay. TourSense demonstrates how the transport data can overcome these limitations and provide better insights for different stakeholders, typically including tour agencies, transport operators, and tourists themselves. Specifically, we first propose a graph-based iterative propagation learning algorithm to recognize tourists from public commuters. Taking advantage of the trace data from the identified tourists, we then design a tourist preference analytics model to learn and predict their next tour, where an interactive user interface is implemented to ease the information access and gain the insights from the analytics results. Experiments with real-world datasets (from over 5.1 million commuters and their 462 million trips) show the promise and effectiveness of the proposed framework: the Macro and Micro F1 scores of the tourist identification system achieve 0.8549 and 0.7154, respectively, whereas the tourist preference analytics system improves the baselines by at least 23.53 and 11.44 percent in terms of precision and recall. [ABSTRACT FROM AUTHOR]

We develop an adversarial learning algorithm for supervised classification in general and Convolutional Neural Networks (CNN) in particular. The algorithm's objective is to produce small changes to the data distribution defined over positive and negative class labels so that the resulting data distribution is misclassified by the CNN. The theoretical goal is to determine a manipulating change on the input data that finds learner decision boundaries where many positive labels become negative labels. Then we propose a CNN which is secure against such unforeseen changes in data. The algorithm generates adversarial manipulations by formulating a multiplayer stochastic game targeting the classification performance of the CNN. The multiplayer stochastic game is expressed in terms of multiple two-player sequential games. Each game consists of interactions between two players—an intelligent adversary and the learner CNN—such that a player's payoff function increases with interactions. Following the convergence of a sequential noncooperative Stackelberg game, each two-player game is solved for the Nash equilibrium. The Nash equilibrium finds a pair of strategies (learner weights and evolutionary operations) from which there is no incentive for either learner or adversary to deviate. We then retrain the learner over all the adversarial manipulations generated by multiple players to propose a secure CNN which is robust to subsequent adversarial data manipulations. The adversarial data and corresponding CNN performance is evaluated on MNIST handwritten digits data. The results suggest that game theory and evolutionary algorithms are very effective in securing deep learning models against performance vulnerabilities simulated as attack scenarios from multiple adversaries. [ABSTRACT FROM AUTHOR]

Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow. Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful over- and under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners. [ABSTRACT FROM AUTHOR]

Abstract: The aims of this paper are: to compare different data mining algorithms for modelling soil visible–near infrared (vis–NIR: 350–2500nm) diffuse reflectance spectra and to assess the interpretability of the results. We compared multiple linear regression (MLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), support vector machines (SVM), random forests (RF), boosted trees (BT) and artificial neural networks (ANN) to estimate soil organic carbon (SOC), clay content (CC) and pH measured in water (pH). The comparisons were also performed using a selected set of wavelet coefficients from a discrete wavelet transform (DWT). Feature selection techniques to reduce model complexity and to interpret and evaluate the models were tested. The dataset consists of 1104 samples from Australia. Comparisons were made in terms of the root mean square error (RMSE), the corresponding R 2 and the Akaike Information Criterion (AIC). Ten-fold-leave-group out cross validation was used to optimise and validate the models. Predictions of the three soil properties by SVM using all vis–NIR wavelengths produced the smallest RMSE values, followed by MARS and PLSR. RF and especially BT were out-performed by all other approaches. For all techniques, implementing them on a reduced number of wavelet coefficients, between 72 and 137 coefficients, produced better results. Feature selection (FS) using the variable importance for projection (FSVIP) returned 29–31 selected features, while FSMARS returned between 11 and 14 features. DWT–ANN produced the smallest RMSE of all techniques tested followed by FSVIP–ANN and FSMARS–ANN. However, both the FSVIP–ANN and FSMARS–ANN models used a smaller number of features for the predictions than DWT–ANN. This is reflected in their AIC, which suggests that, when both the accuracy and parsimony of the model are taken into consideration, the best SOC model was the FSMARS–ANN, and the best CC and pH models were those from FSVIP–ANN. Analysis of the selected bands shows that: (i) SOC is related to wavelengths indicating C―O, C═O, and N―H compounds, (ii) CC is related to wavelengths indicating minerals, and (iii) pH is related to wavelengths indicating both minerals and organic material. Thus, the results are sensible and can be used for comparison to other soils. A systematic comparison like the one presented here is important as the nature of the target function has a strong influence on the performance of the different algorithms. [Copyright &y& Elsevier]

An unsupervised multi-strategy approach has been developed to identify informative genes from high throughput genomic data. Several statistical methods have been used in the field to identify differentially expressed genes. Since different methods generate different lists of genes, it is very challenging to determine the most reliable gene list and the appropriate method. This paper presents a multi-strategy method, in which a combination of several data analysis techniques are applied to a given dataset and a confidence measure is established to select genes from the gene lists generated by these techniques to form the core of our final selection. The remainder of the genes that form the peripheral region are subject to exclusion or inclusion into the final selection. This paper demonstrates this methodology through its application to an in-house cancer genomics dataset and a public dataset. The results indicate that our method provides more reliable list of genes, which are validated using biological knowledge, biological experiments, and literature search. We further evaluated our multi-strategy method by consolidating two pairs of independent datasets, each pair is for the same disease, but generated by different labs using different platforms. The results showed that our method has produced far better results. [ABSTRACT FROM AUTHOR]

Data mining and forecasting has attracted a lot of research interest in time series data sequence. The paper brings a new insight in how to select different time series forecasting models to make prediction according to different situations and data patterns. The contribution of this research is to facilitate prediction modelling system design for extensive forecasting purpose. [ABSTRACT FROM AUTHOR]

The importance of data mining methods has increased dramatically in recent years, making this research area relevant and challenging to extract actionable knowledge from complex data. Indeed, new algorithms and machine learning methods are constantly being explored to deal with domains that present multiple challenges including high-dimensionality, heterogeneity of features, and complex relationships between data objects. This special issue aims at discussing emerging approaches for learning from complex data, including text data, images, and social media data. [ABSTRACT FROM AUTHOR]