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Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Each of the three improved models are designed to mimic the excitation and inhibition activity of neurons. The improved model proposed in this paper is shown to be effective in the experimental part. All three models and original DNM have their own strengths, so it can be considered that the new model proposed in this paper well enriches the diversity of neuronal models and contributes to future research on networks models. [ABSTRACT FROM AUTHOR]

The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies. [ABSTRACT FROM AUTHOR]

In recent years, the number of similar software products with many common parts has been increasing due to the reuse and plagiarism of source code in the software development process. Pattern matching, which is an existing method for detecting similarity, cannot detect the similarities between these software products and other programs. It is necessary, for example, to detect similarities based on commonalities in both functionality and control structures. At the same time, detailed software analysis requires manual reverse engineering. Therefore, technologies that automatically identify similarities among the arge amounts of code present in software products in advance can reduce these oads. In this paper, we propose a representation earning model to extract feature expressions from assembly code obtained by statically analyzing such code to determine the similarity between software products. We use assembly code to eliminate the dependence on the existence of source code or differences in development anguage. The proposed approach makes use of Asm2Vec, an existing method, that is capable of generating a vector representation that captures the semantics of assembly code. The proposed method also incorporates information on the program control structure. The control structure can be represented by graph data. Thus, we use graph embedding, a graph vector representation method, to generate a representation vector that reflects both the semantics and the control structure of the assembly code. In our experiments, we generated expression vectors from multiple programs and used clustering to verify the accuracy of the approach in classifying similar programs into the same cluster. The proposed method outperforms existing methods that only consider semantics in both accuracy and execution time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new technique for handling a class of sparse supervised learning problems, specifically focusing on regression, binary, and multi-class classification problems. Sparse regression problems are associated with datasets including both arithmetic and categorical features and by encoding the dataset leads to an underdetermined sparse linear system. Furthermore, logistic regression can be used for both sparse binary and multi-class classification problems, solving an underdetermined sparse linear system. A new technique called Sparse Approximate Pseudoinverse Preconditioning (SAPP), namely the Explicit Preconditioned Conjugate Gradient for Normal Equations (EPCGNE) method based on Generic Approximate Sparse Pseudoinverse matrices is introduced for solving underdetermined sparse least square problems. Numerical experiments were carried out demonstrating a significant improvement of the performance metrics for the proposed SAPP scheme compared to other learners. [ABSTRACT FROM AUTHOR]

Fixed-point theory plays many important roles in real-world problems, such as image processing, classification problem, etc. This paper introduces and analyzes a new, accelerated common-fixed-point algorithm using the viscosity approximation method and then employs it to solve convex bilevel optimization problems. The proposed method was applied to data classification with the Diabetes, Heart Disease UCI and Iris datasets. According to the data classification experiment results, the proposed algorithm outperformed the others in the literature. [ABSTRACT FROM AUTHOR]

Learning Algorithm for Multivariable Data Analysis (LAMDA) is a fuzzy approach, which has been used in clustering and classification processes. Recently, extensions have been proposed for LAMDA, to improve its performance in classification tasks. The first one is called LAMDA-FAR, which proposes a new criterion to validate functional states after recognition, based on the minimum and maximum calculated distances between the two membership degrees with the highest values. The second extension is called LAMDA-HAD, which proposes two strategies to improve LAMDA performance. The first strategy calculates an adaptive Global Adequacy Degree (GAD) of the Non-Informative Class (NIC) to each class to prevent that correctly classified individuals will be assigned to the NIC class. The second strategy calculates the similarity among the GAD of an individual and all ones of each class, to make a more reliable assignment. This article analyzes the performance of these techniques for different classification problems. The goal is to define the application context for each one. Each case study was defined by a set of data in an operational context, which must be used by the classification techniques to obtain accurate results. LAMDA-HAD was better with unbalanced classes, while LAMDA-FAR was excellent for discovering new classes. Both algorithms worked well for different levels of noise (which can represent faults in the sensors), a factor important in diagnostic tasks. The aim of this paper is to determine the correct utilization profile of each LAMDA technique adjusted to the properties of the problems under study. [ABSTRACT FROM AUTHOR]

The Stirling numbers for graphs provide a combinatorial interpretation of the number of cycle covers in a given graph. The problem of generating all cycle covers or enumerating these quantities on general graphs is computationally intractable, but recent work has shown that there exist infinite families of sparse or structured graphs for which it is possible to derive efficient enumerative formulas. In this paper, we consider the case of trees and forests of a fixed size, proposing an efficient algorithm based on matrix algebra to approximate the distribution of Stirling numbers. We also present a model application of machine learning to enumeration problems in this setting, demonstrating that standard regression techniques can be applied to this type of combinatorial structure. [ABSTRACT FROM AUTHOR]

In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets. The refinement of the BN-S model is obtained with various machine and deep learning algorithms. The refinement leads to the extraction of a deterministic parameter from the empirical data set. The problem is transformed to an appropriate classification problem with a couple of different approaches — the volatility approach and the duration approach. The analysis is implemented to the Bakken crude oil data and the aforementioned deterministic parameter is obtained for a wide range of data sets. With the implementation of this parameter in the refined model, the resulting model performs much better than the classical BN-S model. [ABSTRACT FROM AUTHOR]

In the development of algorithms for convex optimization problems, symmetry plays a very important role in the approximation of solutions in various real-world problems. In this paper, based on a fixed point algorithm with the inertial technique, we proposed and study a new accelerated algorithm for solving a convex bilevel optimization problem for which the inner level is the sum of smooth and nonsmooth convex functions and the outer level is a minimization of a smooth and strongly convex function over the set of solutions of the inner level. Then, we prove its strong convergence theorem under some conditions. As an application, we apply our proposed algorithm as a machine learning algorithm for solving some data classification problems. We also present some numerical experiments showing that our proposed algorithm has a better performance than the five other algorithms in the literature, namely BiG-SAM, iBiG-SAM, aiBiG-SAM, miBiG-SAM and amiBiG-SAM. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new accelerated common fixed-point algorithm for two countable families of G-nonexpansive mappings. Weak convergence results are obtained in the context of directed graphs in real Hilbert spaces. As applications, we apply the obtained results to solving some convex minimization problems and employ our proposed algorithm to solve the data classification of Breast Cancer, Heart Diseases and Ionosphere. Moreover, we also compare the performance of our proposed algorithm with other algorithms in the literature and it is shown that our algorithm has a better convergence behavior than the others. [ABSTRACT FROM AUTHOR]

Cat-SD (Categorization by Similarity-Dissimilarity) is a method developed for handling nominal classification problems in the context of Multiple Criteria Decision Aiding (MCDA). This paper describes the design and implementation of this method and an application dealing with a recruitment and selection process in the Special Forces of the Portuguese Army. Besides, it proposes interaction protocols to elicit the preference parameters of the method to facilitate the construction of a decision model when the analyst guides the decision maker. Cat-SD has been implemented in DecSpace, a user-friendly online platform for supporting decision aiding processes using one or more MCDA methods simultaneously. The study related to the Portuguese Army Special Forces recruitment and selection demonstrates how these protocols and a tool like DecSpace can facilitate the process of applying the method in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods. A model based on the neural network multi-classification algorithmis constructed to find the minimum number of Gaussian quadrature points satisfying the given accuracy. The constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is selected. The two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model, and the accuracy of the model is about 90%. Finally, by incorporating the predicted Gaussian quadrature points into the boundary element analysis, we find that the numerical solution and the analytical solution are in good agreement, which verifies the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

We analyze a classification problem of mentally pronounced Russian phonemes based on data obtained by means of an electroencephalography device. We describe the data collection method as well as the methods of the obtained data processing. To solve the small sample size problem we present the augmentation techniques that use the time stretching and the white noise adding. Our approach uses an algorithm based on the convolutional neural networks and it is applicable to solving the binary and multiclass classification problems. The conducted experiments allow us to estimate the accuracy of our algorithms and to compare them to the existing algorithms based on the support vector machine. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a new approach, based on fuzzy goal programming, for classification problems where the cut-off value c corresponding to the discriminant axe is considered as imprecise. The fuzziness was handled through different membership functions. The proposed model will be illustrated through two and multi-groups classification problems. [ABSTRACT FROM AUTHOR]

In this research, we study convex bi-level optimization problems for which the inner level consists of the sum of two proper, convex, and lower semi-continuous functions. We propose and analyze a new accelerated forward-backward algorithm using linesearch and inertial techniques for solving a solution of convex bi-level optimization. We then establish a strong convergence theorem of the proposed method under some suitable conditions. As an application, we apply our algorithm to solving data classifications of some non-communicable diseases. We conduct a comparative analysis with existing algorithms to show the effectiveness of our algorithm. Our numerical experiments confirm that our proposed algorithm outperforms other methods in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a GA method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to find a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown. [ABSTRACT FROM AUTHOR]

This paper proposes a distance-based index to evaluate the input data and the data flowing through layers of a convolutional neural network in classification problems. This shape-less index is proposed based on the class separability and data complexity concepts which are explained in a classification problem. A dataset is complex if many of its samples are located in overlapping regions belonging to different classes, making the classification challenging. In addition, a class is separable if its samples are not located in the overlapped area. The proposed index counts the number of samples in the overlapped area normalized by the number of all samples, measuring the complexity of the dataset. The paper explains the idea of dividing features into exclusive and common features, and identifies the conditions for separability based on this concept. After that, probability evidence shows why the Separation index can be included in complexity measurement indices. The benefits of using the introduced index are shown through many case studies. In the first case study, the proposed index ranks some known supervised datasets from less to more challenging. In the second case study, the separability of Dataflow is evaluated through layers of two pre-trained VGG-16 networks in the classification of CIFAR-10, CIFAR-100, and Caltech-101. The evaluation of Dataflow is also shown through a trained ResNet-18 model for the Fashion-MNIST dataset in the subsequent case study. Finally, it is shown that the correct classification rate and the separation index are almost equivalent through layers, particularly when the separation index is increased. [ABSTRACT FROM AUTHOR]

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Despite the effectiveness of deep neural networks, feed-forward neural networks (FFNNs) continue to play a crucial role in many applications, especially when dealing with limited data availability. The primary challenge in FFNNs is determining the optimal weights during the training process, aiming to minimise the disparity between actual and predicted outputs. Although gradient-based techniques like backpropagation (BP) have traditionally been popular for FFNN training, they come with inherent limitations, such as sensitivity to initial weights and susceptibility to getting trapped in local optima. To overcome these challenges, we introduce a novel approach based on the Gaining-Sharing Knowledge-based(GSK) algorithm. To the best of our knowledge, this paper represents the first exploration of GSK for neural network training. After obtaining the appropriate weights for the FFNN by the GSK, the weights and biases are utilised to initialise a Levenberg–Marquardt backpropagation (LMBP) algorithm, serving as a local search component. In other words, our proposed algorithm, GSK-LocS, leverages the global search capabilities of the GSK algorithm and combines them with the local search capabilities of LMBP for neural network training. This integration mitigates sensitivity to initial values and reduces the risk of being trapped in local optima. Experimental results conducted on classification and approximation problems provide compelling evidence that our proposed algorithm is highly competitive compared to other existing methods.

Feature selection can sort out useful features to obtain good performance when dealing with high-dimensional data. Feature selection methods based on support vector data description (SVDD) have been proposed for one-class classification problems: SVDD-radius-recursive feature elimination (SVDD-RRFE) and SVDD-dual-objective-recursive feature elimination (SVDD-DRFE). However, both SVDD-RRFE and SVDD-DRFE use only one-class samples even given a multi-class classification task, and suffer from high computational complexity. To remedy it, this paper extends both SVDD-RRFE and SVDD-DRFE to binary and multi-class classification problems using multiple SVDD models, and proposes fast feature ranking schemes for them in the case of the linear kernel. Experimental results on toy, UCI and microarray datasets show the efficiency and the feasibility of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to study different notions of Sobolev capacity commonly used in the analysis of obstacle‐ and Signorini‐type variational inequalities. We review basic facts from capacity theory in an abstract setting that is tailored to the study of W1,p ‐ and W1–1/p,p‐capacities, and we prove equivalency results that relate several approaches found in the literature to each other. Motivated by applications in contact mechanics, we especially focus on the behavior of different Sobolev capacities on and near the boundary of the domain in question. As a result, we obtain, for example, that the most common approaches to the sensitivity analysis of Signorini‐type problems are exactly the same. [ABSTRACT FROM AUTHOR]

In recent years, classification problems have been widely studied as one of the critical research directions in artificial intelligence. Efficiently simulating the human brain's ability to solve deterministic classification problems has become a prominent research branch in the field of classification problems. Numerical spiking neural P systems (NSN P systems) with the characteristics of the highly flexible spiking neural P (SNP)systems architecture and addressing numerical variants have special advantages for solving classification problems in the real world. This paper proposes, a novel NSN P system, namely, a learning numerical spiking neural P system (LNSN P system), is proposed to solve classification problems. In LNSN P systems, each neuron contains not only discrete spikes and rules that must be described by regular expressions but also numerical variables and production functions that can be in any form, overcoming the discontinuity of traditional SN P systems. In addition, an ascending structure without an outer encoder has been designed in this study to encode information from low-dimensional indivisible space to high-dimensional divisible space. At the same time, an adaptive learning mechanism has been introduced to improve algorithm convergence and precision. Finally, the experimental results obtained using eight benchmark datasets from the machine learning repository demonstrate the feasibility and effectiveness of the proposed LNSN P system. Moreover, the proposed LNSN P system exhibits lower space complexity and better generalisation ability than the existing SN P systems with the learning ability to solve different classification problems. • An LNSN P system is proposed to solve classification problems. • An ascending structure without an outer encoder is designed to encode information. • An adaptive learning mechanism is introduced to improve algorithm performance. [ABSTRACT FROM AUTHOR]

Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Each of the three improved models are designed to mimic the excitation and inhibition activity of neurons. The improved model proposed in this paper is shown to be effective in the experimental part. All three models and original DNM have their own strengths, so it can be considered that the new model proposed in this paper well enriches the diversity of neuronal models and contributes to future research on networks models.

In this paper, we present a new method for dealing with feature subset selection based on fuzzy entropy measures for handling classification problems. First, we discretize numeric features to construct the membership function of each fuzzy set of a feature. Then, we select the feature subset based on the proposed fuzzy entropy measure focusing on boundary samples, The proposed method can select relevant features to get higher average classification accuracy rates than the ones selected by the MIFS method (Battiti, R. in IEEE Trans. Neural Netw. 5(4):537-550, 1994), the FQI method (De, R.K., et al. in Neural Netw. 12(10):1429-1455, 1999), the OFEI method, Dong-and-Kothari's method (Dong, M., Kothari, R. in Pattern Recognit. Lett. 24(9):1215-1225, 2003) and the OFFSS method (Tsang, E.C.C., et al. in IEEE Trans. Fuzzy Syst. I I (2):202-213, 2003). [ABSTRACT FROM AUTHOR]

Abstract The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies.

This paper presents a constructive approach to estimating the size of a neural network necessary to solve a given classification problem. The results are derived using an information entropy approach in the context of limited precision integer weights. Such weights are particularly suited for hardware implementations since the area they occupy is limited, and the computations performed with them can be efficiently implemented in hardware. The considerations presented use an information entropy perspective and calculate lower bounds on the number of bits needed in order to solve a given classification problem. These bounds are obtained by approximating the classification hypervolumes with the volumes of several regular (i.e., highly symmetric) n-dimensional bodies. The bounds given here allow the user to choose the appropriate size of a neural network such that: (i) the given classification problem can be solved, and (ii) the network architecture is not oversized. All considerations presented take into account the restrictive case of limited precision integer weights, and therefore can be directly applied when designing VLSI implementations of neural networks. [ABSTRACT FROM AUTHOR]

Classification problems have been introduced by M. Ziegler as a generalization of promise problems. In this paper we are concerned with solvability and unsolvability questions with respect to a given set or language family, especially with cores of unsolvability. We generalize the results about unsolvability cores in promise problems to classification problems. Our main results are a characterization of unsolvability cores via cohesiveness and existence theorems for such cores in unsolvable classification problems. In contrast to promise problems we have to strengthen the conditions to assert the existence of such cores. In general unsolvable classification problems with more than two components exist, which possess no cores, even if the set family under consideration satisfies the assumptions which are necessary to prove the existence of cores in unsolvable promise problems. But, if one of the components is fixed we can use the results on unsolvability cores in promise problems, to assert the existence of such cores in general. In this case we speak of conditional classification problems and conditional cores. The existence of conditional cores can be related to complexity cores. Using this connection we can prove for language families, that conditional cores with recursive components exist, provided that this family admits an uniform solution for the word problem. [ABSTRACT FROM AUTHOR]

In this paper, we present a bio-inspired learning methodology based on swarm particle optimization to learn both weights and topology of a multilayer feedforward neural network. The training algorithm represents a novel adaptive version of the particle swarm algorithm where the inertia weight is improved to increase the accuracy of the neural network. In addition to the updated exploration parameter, the proposed algorithm encloses a new acceleration parameter to deal with the convergence rate. In fact, the adopted optimization strategy aims to simulate a mutation rate with higher values in the favor of a global search. The swarm-based feedforward neural network was tested with benchmarking problems which includes both classification and regression problems. Some results are also presented to evaluate the algorithm performances. [ABSTRACT FROM AUTHOR]

PROMETHEE methods based on the outranking relation theory are extensively used in multicriteria decision aid. A preference index representing the intensity of preference for one pattern over another pattern can be measured by various preference functions. The higher the intensity, the stronger the preference is indicated. In contrast to traditional single-layer perceptrons (SLPs) with the sigmoid function, this paper develops a novel PROMETHEE II-based SLP using concepts from the PROMETHEE II method involving pairwise comparisons between patterns. The assignment of a class label to a pattern is dependent on its net preference index, which the proposed perceptron obtains. Specially, this study designs a genetic-algorithm-based learning algorithm to determine the relative weights of respective criteria in order to derive the preference index for any pair of patterns. Computer simulations involving several real-world data sets reveal the classification performance of the proposed PROMETHEE II-based SLP. The proposed perceptron performs well compared to the other well-known fuzzy or non-fuzzy classification methods. [ABSTRACT FROM AUTHOR]

In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles. [ABSTRACT FROM AUTHOR]

In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi's method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed. [ABSTRACT FROM AUTHOR]

Data cleaning is an important step in the data mining process. Successful data mining applications require good quality data. In this paper, we propose a data cleaning technique that smoothes out a substantial amount of attribute noise and handles missing attribute values as well. Our approach is inspired by the Expectation-Maximization (EM) algorithm. It iteratively refines each attribute-value using a predictor constructed from the previously refined values (known values in the first iteration). We demonstrate the effectiveness of our technique in smoothing out attribute noise and corroborate the efficacy of our technique by showing improved classification accuracy on a number of real world data sets from UCI repository [2]. Moreover, we show that our technique can easily be adapted to fill up missing attribute-values in classification problems more effectively than other standard approaches. [ABSTRACT FROM AUTHOR]

Ensemble methods have been known to improve the prediction accuracy over the base learning algorithms. AdaBoost is well-recognized for this in its class. However, it is susceptible to overfitting the training instances corrupted by class label noise. This paper proposes a modification of AdaBoost that is more tolerant to class label noise, which further enhances its ability to boost the prediction accuracy. Particularly, we observe that in Adaboost, the weight-hike of noisy examples can be constrained by careful application of a cut-off in their weights. We study the characteristics of our technique empirically using some artificially generated data set. We also corroborate this on a number of data sets from UCI repository [1]. In both experimental settings, the results obtained affirm the efficiency of our approach. Finally, some of the significant characteristics of our technique related to noisy environments have been investigated. [ABSTRACT FROM AUTHOR]

In this paper, we describe the development of kernel-based Support Vector Machine (SVM) classifiers to aid the early diagnosis of acute myocardial infarction (AMI). In particular, we have to recognize if a chest pain, complained by the patient, may be considered the sign of a myocardial infarction or it is the evidence of some other causes. This is a quite difficult medical decision problem, since chest pain is characterized by low specificity (typical values between 30% and 40%) as a symptom associated with myocardial infarction. Moreover, in order to make an objective and accurate diagnosis, the physician has to evaluate a large set of data coming from the patient. These aspects motivated the use of machine learning methodologies, with the aim to support the physician and increase the quality of the diagnostic decision. To this end, we formulated the medical decision problem as a supervised binary classification problem (AMI class and not AMI class), by developing a training set with 242 cases (130 in the AMI class and 112 in the not AMI class), each case characterized by a set of 105 features. We also considered a feature selection procedure, by selecting 25 of the 105 features. By the framework of generalized SVM model, we tested and validated the behavior of three kernel functions: Polynomial, Gaussian and Laplacian. By running a 10-fold cross validation procedure, the performance of the best tested classifier was 97.5%. By the same 10-fold cross validation procedure, we tested linear and quadratic discriminant analysis classifiers, with testing correctness of 86.8% and 94%, respectively. The numerical results demonstrate the effectiveness and robustness of the proposed approaches for solving the relevant medical decision making problem. [ABSTRACT FROM AUTHOR]

Given a set S of n line segments in the plane, we say that a region R ⊆ R 2 is a stabber for S if R contains exactly one endpoint of each segment of S . In this paper we provide optimal or near-optimal algorithms for reporting all combinatorially different stabbers for several shapes of stabbers. Specifically, we consider the case in which the stabber can be described as the intersection of axis-parallel halfplanes (thus the stabbers are halfplanes, strips, quadrants, 3-sided rectangles, or rectangles). The running times are O ( n ) (for the halfplane case), O ( n log  n ) (for strips, quadrants, and 3-sided rectangles), and O ( n 2 log  n ) (for rectangles). [ABSTRACT FROM AUTHOR]

In recent years, the number of similar software products with many common parts has been increasing due to the reuse and plagiarism of source code in the software development process. Pattern matching, which is an existing method for detecting similarity, cannot detect the similarities between these software products and other programs. It is necessary, for example, to detect similarities based on commonalities in both functionality and control structures. At the same time, detailed software analysis requires manual reverse engineering. Therefore, technologies that automatically identify similarities among the arge amounts of code present in software products in advance can reduce these oads. In this paper, we propose a representation earning model to extract feature expressions from assembly code obtained by statically analyzing such code to determine the similarity between software products. We use assembly code to eliminate the dependence on the existence of source code or differences in development anguage. The proposed approach makes use of Asm2Vec, an existing method, that is capable of generating a vector representation that captures the semantics of assembly code. The proposed method also incorporates information on the program control structure. The control structure can be represented by graph data. Thus, we use graph embedding, a graph vector representation method, to generate a representation vector that reflects both the semantics and the control structure of the assembly code. In our experiments, we generated expression vectors from multiple programs and used clustering to verify the accuracy of the approach in classifying similar programs into the same cluster. The proposed method outperforms existing methods that only consider semantics in both accuracy and execution time.

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed.

Classification is one of the important tasks in data mining. The probabilistic neural network (PNN) is a well-known and efficient approach for classification. The objective of the work presented in this paper is to build on this approach to develop an effective method for classification problems that can find high-quality solutions (with respect to classification accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridizes the firefly algorithm with simulated annealing (denoted as SFA), where simulated annealing is applied to control the randomness step inside the firefly algorithm while optimizing the weights of the standard PNN model. We also extend our work by investigating the effectiveness of using Lévy flight within the firefly algorithm (denoted as LFA) to better explore the search space and by integrating SFA with Lévy flight (denoted as LSFA) in order to improve the performance of the PNN. The algorithms were tested on 11 standard benchmark datasets. Experimental results indicate that the LSFA shows better performance than the SFA and LFA. Moreover, when compared with other algorithms in the literature, the LSFA is able to obtain better results in terms of classification accuracy. [ABSTRACT FROM AUTHOR]