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Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasized the requirement to multiple kernel learning. This paper proposes a novel regression...
Measures of relevance between features play an important role in classification and regression analysis. Mutual information has been proved to be an effective measure for categorical features. However, there is a limitation in computing relevance between numerical features with mutual information. In this work, we generalize Shannon's information entropy to neighborhood information entropy and propose...
Bankruptcy prediction has been one of the most challenging tasks and a major research topic in accounting and finance. In this paper, bagging ensemble, a popular technique in the machine learning community, is proposed to improve the prediction performance of artificial neural networks in bankruptcy prediction analysis. The experiments conducted on the public dataset show that the proposed approach...
In recent years the use of personalized service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. In literature a number of classification algorithms have been used to classify user related information to create accurate user profiles. Nevertheless, there is lack of comparison of these algorithms with classification...
Ant colony optimization (ACO) is a kind of bionic swarm intelligence algorithm belongs to artificial intelligence (AI) field and has been successfully applied in resolving complex optimization problems. Support vector machine (SVM) is a new machine learning method with greater generalization performance, and has shown its superiority in classification and regression problems. By combining the advantages...
Support Vector Machines (SVMs) have become an increasingly popular tool for machine learning tasks involving classification and regression, and have shown superior performance compared to other machine learning techniques. In this paper we propose a hybrid classification technique to extract fuzzy rules from the support vector machine and evaluate the rules against decision tree classifier constructed...
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression,...
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