Feature selection for multi-label naive Bayes classification |
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Authors: | Min-Ling Zhang José M Peña Victor Robles |
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Affiliation: | a College of Computer and Information Engineering, Hohai University, Nanjing 210098, China b National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China c Department of Computer Architecture and Technology, Technical University of Madrid, Madrid, Spain |
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Abstract: | In multi-label learning, the training set is made up of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances. In this paper, this learning problem is addressed by using a method called Mlnb which adapts the traditional naive Bayes classifiers to deal with multi-label instances. Feature selection mechanisms are incorporated into Mlnb to improve its performance. Firstly, feature extraction techniques based on principal component analysis are applied to remove irrelevant and redundant features. After that, feature subset selection techniques based on genetic algorithms are used to choose the most appropriate subset of features for prediction. Experiments on synthetic and real-world data show that Mlnb achieves comparable performance to other well-established multi-label learning algorithms. |
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Keywords: | Multi-label learning Naive Bayes Feature selection Principal component analysis Genetic algorithm |
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