Abstract
Based on the research on feature relevance, features can be divided into four categories: strong relevance, weak relevance, irrelevance and redundancy. Feature selection is a process of removing irrelevance and redundancy features in nature. A feature selection algorithm is given, which uses dynamic mutual information as evaluation criteria and eliminates irrelevance and redundancy features by approximate Markov Blanket. Experimental results on UCI data sets with support vector machine as the classifier indicate the feasibility and validity of the algorithm proposed in this paper.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, X., Yao, X., Zhang, Y., Lei, L. (2012). A Feature Selection Algorithm Based on Approximate Markov Blanket and Dynamic Mutual Information. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_29
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DOI: https://doi.org/10.1007/978-3-642-31919-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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