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Multimodal Bare-Bone Niching Differential Evolution in Feature Selection
Feature selection (FS) is to select relevant feature subsets from a given feature set to reduce the dimensions of data. In the classification task, feature selection can not only reduce the calculation cost but also improve the classification accuracy. In this paper, a multimodal bare-bone niching d...
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Main Authors: | , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | Feature selection (FS) is to select relevant feature subsets from a given feature set to reduce the dimensions of data. In the classification task, feature selection can not only reduce the calculation cost but also improve the classification accuracy. In this paper, a multimodal bare-bone niching differential evolution algorithm (MBNDE) is proposed for FS in classification problems. MBNDE finds multiple feature combinations that can achieve the highest classification rate. Three different strategies have been applied to MBNDE in the niching process, and the corresponding algorithms are termed MBNDE-CC (crowding and cluster), MBNDE-CS (crowding and speciation), as well as MBNDE (index-based). A modified 3-NN classifier is designed to evaluate the classification rate of different feature combinations. The results of the proposed method and some existing multimodal and unimodal algorithms for FS are compared. The proposed algorithm shows promising performance. |
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ISSN: | 2577-1655 |
DOI: | 10.1109/SMC52423.2021.9658633 |