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With the advent of Big Data, data is being collected at an unprecedented fast pace, and it needs to be processed in a short time. To deal with data streams that flow continuously, classical batch learning algorithms cannot be applied and it is necessary to employ online approaches. Online learning consists of continuously revising and refining a model by incorporating new data as they arrive, and it allows important problems such as concept drift or management of extremely high-dimensional datasets to be solved. In this paper, we present a unified pipeline for online learning which covers online discretization, feature selection and classification. Three classical methods—the k -means discretizer, the χ 2 filter and a one-layer artificial neural network—have been reimplemented to be able to tackle online data, showing promising results on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Abstract: Feature selection and classification techniques have been studied independently without considering the interaction between both procedures, which leads to a degraded performance. In this paper, we present a new neural network approach, which is called an algorithm learning based neural network (ALBNN), to improve classification accuracy by integrating feature selection and classification procedures. In general, a knowledge-based artificial neural network operates on prior knowledge from domain experience, which provides it with better starting points for the target function and leads to better classification accuracy. However, prior knowledge is usually difficult to identify. Instead of using unknown background resources, the proposed method utilizes prior knowledge that is mathematically calculated from the properties of other learning algorithms such as PCA, LARS, C4.5, and SVM. We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning. ALBNN correctly approximates a target hypothesis by both considering the interaction between two procedures and minimizing individual procedure errors. The approach produces new relevant features and improves the classification accuracy. Experimental results exhibit improved performance in various classification problems. ALBNN can be applied to various fields requiring high classification accuracy. [Copyright &y& Elsevier]

Abstract: Feature selection is one of the core issues in designing pattern recognition and machine learning systems, and has attracted considerable attention in the literature. In this paper, a new feature subset selection algorithm with conditional mutual information is proposed, which firstly guarantees to find a subset of which the mutual information with the class is the same as that of the original set of features, and then eliminates potential redundant features from the view of minimal information loss based on the cumulate conditional mutual information minimization criterion. From the reliability point of view, this criterion can also abate the disturbance caused by sample insufficiency in conditional mutual information estimation. In addition, a fast implementation of conditional mutual information estimation is proposed and used to tackle the computationally intractable problem. Empirical results verify that our algorithm is efficient and achieves better accuracy than several representative feature selection algorithms for three typical classifiers on various datasets. [Copyright &y& Elsevier]

Abstract: The inclusion of irrelevant, redundant, and inconsistent features in the data-mining model results in poor predictions and high computational overhead. This paper proposes a novel information theoretic-based interact (IT-IN) algorithm, which concerns the relevance, redundancy, and consistency of the features. The proposed IT-IN algorithm is compared with existing Interact, FCBF, Relief and CFS feature selection algorithms. To evaluate the classification accuracy of IT-IN and remaining four feature selection algorithms, Naïve Bayes, SVM, and ELM classifier are used for ten UCI repository datasets. The proposed IT-IN performs better than existing above algorithms in terms of number of features. The specially designed hash function is used to speed up the IT-IN algorithms and provides minimum computation time than the Interact algorithms. The result clearly reveals that the proposed feature selection algorithm improves the classification accuracy for ELM, Naïve Bayes, and SVM classifiers. The performance of proposed IT-IN with ELM classifier is superior to other classifiers. [ABSTRACT FROM AUTHOR]

Abstract: There are irrelevant features that are redundant or significantly degrade the learning accuracy in the real-world complex classification tasks. This paper presents a new hybrid learning algorithm based on a cooperative coevolutionary algorithm (Co-CEA) with dual populations for designing the radial basis function neural network (RBFNN) models with an explicit feature selection. This approach attempts to complete both the RBFNN construction and the feature selection simultaneously. The proposed algorithm utilizes the Co-CEA’s divide-and-cooperative mechanism, which utilizes the evolutionary algorithms executing in parallel to coevolve subpopulations, corresponding to the hidden layer structure and the dominate features respectively. The algorithm adopts the binary encoding to represent the feature subset and the matrix-form mixed encoding to represent the RBFNN hidden layer structure, and a complete solution is formed via collaborations among the two subpopulations. Experimental results illustrate that the proposed algorithm outperforms other algorithms in references in terms of the classification accuracy, and it is able to obtain both prominent features and good RBFNN structure with higher prediction capability. [Copyright &y& Elsevier]

Abstract: Algorithms for feature selection in predictive data mining for classification problems attempt to select those features that are relevant, and are not redundant for the classification task. A relevant feature is defined as one which is highly correlated with the target function. One problem with the definition of feature relevance is that there is no universally accepted definition of what it means for a feature to be ‘highly correlated with the target function or highly correlated with the other features’. A new feature selection algorithm which incorporates domain specific definitions of high, medium and low correlations is proposed in this paper. The proposed algorithm conducts a heuristic search for the most relevant features for the prediction task. [Copyright &y& Elsevier]