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With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM‐CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM‐CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM‐CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM‐CART model in supporting decision‐making tasks. [ABSTRACT FROM AUTHOR]

In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min–Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is to formulate an accurate and useful learning model that is able to perform online clustering and to explain its predictions. The online clustering capability is inherited from the FMM hyperboxes, while the explanatory capability arises from the underlying decision trees of ECT. Four different mean measures, namely harmonic, geometric, arithmetic, and root mean square, are incorporated into FMM for computing its hyperbox centroids. A series of benchmark and real-world data sets are used for evaluating the FMM-ECT performance. The results are analyzed and compared with those from other models. The outcomes indicate that FMM-ECT is able to achieve comparable clustering performances, with the advantage of providing explanations of its predictions using a decision tree. [ABSTRACT FROM AUTHOR]

Challenges in time series classification has attracted attention in the past decade. Although large amounts of labeled data are assumed to be available, in reality, labeled data might be scarce to find in many domains. In this paper, we propose an online semi-supervised multi-channel classifier for time series based on growing neural gas (GNG) learning scheme. The method is able to handle multi-channel time series with variation in dimensions and it introduces a label prediction strategy to minimize misclassification. It measures the similarity of input instance and learned templates using weighted multi-channel dynamic time warping technique and learns the topology of input data space specified for each class using the GNG learning algorithm. Comprehensive evaluation is conducted using various datasets, such as gesture recognition, human activity recognition, and human daily-life activity recognition. Experimental results demonstrate good classification results, with indication that the proposed approach requires only a handful of labeled instances to construct an accurate classification model. [ABSTRACT FROM AUTHOR]

Transcranial Doppler (TCD) is a reliable technique with the advantage of being non-invasive for the diagnosis of cerebrovascular diseases using blood flow velocity measurements pertaining to the cerebral arterial segments. In this study, the recurrent neural network (RNN) is used to classify TCD signals captured from the brain. A total of 35 real, anonymous patient records are collected, and a series of experiments for stenosis diagnosis is conducted. The extracted features from the TCD signals are used for classification using a number of RNN models with recurrent feedbacks. In addition to individual RNN results, an ensemble RNN model is formed in which the majority voting method is used to combine the individual RNN predictions into an integrated prediction. The results, which include the accuracy, sensitivity, and specificity rates as well as the area under the Receiver Operating Characteristic curve, are compared with those from the Random Forest Ensemble model. The outcome positively indicates the usefulness of the RNN ensemble as an effective method for detecting and classifying blood flow velocity changes due to brain diseases. [ABSTRACT FROM AUTHOR]

A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve nonopening pump state. In addition to the noise-free datasets, up to $40\%$ class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise. [ABSTRACT FROM PUBLISHER]

Training neural networks in distinguishing different emotions from physiological signals frequently involves fuzzy definitions of each affective state. In addition, manual design of classification tasks often uses sub-optimum classifier parameter settings, leading to average classification performance. In this study, an attempt to create a framework for multi-layered optimization of an ensemble of classifiers to maximize the system's ability to learn and classify affect, and to minimize human involvement in setting optimum parameters for the classification system is proposed. Using fuzzy adaptive resonance theory mapping (ARTMAP) as the classifier template, genetic algorithms (GAs) were employed to perform exhaustive search for the best combination of parameter settings for individual classifier performance. Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method. Subsequently, the generated population of optimum classifier configurations was used as candidates to form an ensemble of classifiers. Another set of GAs were used to search for the combination of classifiers that would result in the best classification ensemble accuracy. The proposed methodology was tested using two affective data sets and was able to produce relatively small ensembles of fuzzy ARTMAPs with excellent affect recognition accuracy. [ABSTRACT FROM AUTHOR]

The decision making process in communication is affected by internal and external factors from dynamic environments. Humans can perform a variety of behaviors in a similar situation, unlike robots. This paper discusses human psychological phenomena during communication from the point of view of internal and external factors, such as perception, memory, and emotional information. Based on these, we introduce the personality affected robotic emotional model and the emotion affected associative memory model for the robot. We organize an interactive robot system to provide suitable decisions for the robot. Results from interactive communication experiments indicate that the robot is able to perform different actions based on internal and external factors. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid model (FMM-BSO) for feature selection and data classification is proposed. • Fuzzy Min–Max (FMM) is first used for incremental learning of data. • A Brain Storm Optimization (BSO) is then used for feature selection. • The outcome indicates that FMM-BSO is able to produce promising results. Abstract Swarm intelligence (SI)-based optimization methods have been extensively used to tackle feature selection problems. A feature selection method extracts the most significant features and removes irrelevant ones from the data set, in order to reduce feature dimensionality and improve the classification accuracy. This paper combines the incremental learning Fuzzy Min–Max (FMM) neural network and Brain Storm Optimization (BSO) to undertake feature selection and classification problems. Firstly, FMM is used to create a number of hyperboxes incrementally. BSO, which is inspired by the human brainstorming process, is then employed to search for an optimal feature subset. Ten benchmark problems and a real-world case study are conducted to evaluate the effectiveness of the proposed FMM-BSO. In addition, the bootstrap method with the 95% confidence intervals is used to quantify the results statistically. The experimental results indicate that FMM-BSO is able to produce promising results as compared with those from the original FMM network and other state-of-the-art feature selection methods such as particle swarm optimization, genetic algorithm, and ant lion optimization. [ABSTRACT FROM AUTHOR]