Abstract
Feature selection plays an important role in pattern classification. In this paper, a hybrid genetic algorithm (HGA) is adopted to find a subset of the most relevant features. The approach utilizes an improved estimation of the conditional mutual information as an independent measure for feature ranking in the local search operations. It takes account of not only the relevance of the candidate feature to the output classes but also the redundancy between the candidate feature and the already-selected features. Thus, the ability of the HGA to search for the optimal subset of features has been greatly enhanced. Experimental results on a range of benchmark datasets demonstrate that the proposed method can usually find the excellent subset of features on which high classification accuracy is achieved.
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Huang, J., Lv, N., Li, W. (2006). A Novel Feature Selection Approach by Hybrid Genetic Algorithm. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_76
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DOI: https://doi.org/10.1007/978-3-540-36668-3_76
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