An interactive filter-wrapper multi-objective evolutionary algorithm for feature selection

As an important task in data mining, feature selection can improve the performance of classification by eliminating the redundant or irrelevant features in original data. It is mainly divided into filter method and wrapper method, and each one has its own advantages. To make full use of the advantag...

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Vydáno v:Swarm and evolutionary computation Ročník 65; s. 100925
Hlavní autoři: Liu, Zhengyi, Chang, Bo, Cheng, Fan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2021
Témata:
Feature selection
Guiding strategy
Initialization strategy
Multi-objective evolutionary algorithm
Repairing strategy
Repairing strategy
Feature selection
Multi-objective evolutionary algorithm
Initialization strategy
Guiding strategy
ISSN:2210-6502
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Shrnutí:As an important task in data mining, feature selection can improve the performance of classification by eliminating the redundant or irrelevant features in original data. It is mainly divided into filter method and wrapper method, and each one has its own advantages. To make full use of the advantages of two methods, in this paper, an interactive filter-wrapper multi-objective evolutionary algorithm, named GR-MOEA is proposed, where guiding and repairing strategies are used to select feature subsets with high quality. To be specific, a wrapper population and a filter population are evolved simultaneously in the proposed algorithm. To utilize the merits of two populations, an interactive scheme is designed, which includes a wrapper to filter guiding strategy and a filter to wrapper repairing strategy. The guide strategy is to use the good solutions in the wrapper population to steer the filter population towards a better direction. While in the repairing strategy, some features in the wrapper population are repaired by the useful information in filter population, which can avoid the trapping of local optimum in wrapper population. To further enhance the performance of GR-MOEA, two effective initialization strategies are also developed. Empirical studies are conducted by comparing the proposed algorithm with several state-of-the-art on different datasets, and the experimental results demonstrate the superiority of GR-MOEA over the comparison methods in obtaining the feature subsets with higher qualities.
ISSN:2210-6502
DOI:10.1016/j.swevo.2021.100925