Abstract
Feature selection is a very crucial step in data mining process. It aims to find the most important feature subset from a given feature set without degradation of classifying information. As for the traditional feature selection method, the number of candidate feature subsets created by algorithm in an iterative computational way is exponential in the size of the initial attribute set. And relevant algorithm occupies a lot of the system resources in time and space. In this paper, we study and develop a novel feature selection method and provide its mathematic principle, which is based on the factors of attributes contributing to target attribute and their maximum information divergence value (MIDV) to select small enough feature subset and improve the classification accuracy. And then the extensive experiment shows that our proposed method is very efficient in computational performance and scalability than traditional methods.
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Zhang, Z., Shi, Y., Gao, G., Chai, Y. (2008). An Effective Feature Selection Method Using the Contribution Likelihood Ratio of Attributes for Classification. In: Ishikawa, Y., et al. Advanced Web and Network Technologies, and Applications. APWeb 2008. Lecture Notes in Computer Science, vol 4977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89376-9_16
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DOI: https://doi.org/10.1007/978-3-540-89376-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89375-2
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