Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2012.2195000
Title: An effective feature selection method via mutual information estimation
Authors: Yang, J.-B.
Ong, C.-J. 
Keywords: Feature ranking
feature selection
mutual information
random permutation (RP)
Issue Date: 2012
Source: Yang, J.-B., Ong, C.-J. (2012). An effective feature selection method via mutual information estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 (6) : 1550-1559. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2012.2195000
Abstract: This paper proposes a new feature selection method using a mutual information-based criterion that measures the importance of a feature in a backward selection framework. It considers the dependency among many features and uses either one of two well-known probability density function estimation methods when computing the criterion. The proposed approach is compared with existing mutual information-based methods and another sophisticated filter method on many artificial and real-world problems. The numerical results show that the proposed method can effectively identify the important features in data sets having dependency among many features and is superior, in almost all cases, to the benchmark methods. © 1996-2012 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/59438
ISSN: 10834419
DOI: 10.1109/TSMCB.2012.2195000
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