Computer Science ›› 2019, Vol. 46 ›› Issue (7): 157-164.doi: 10.11896/j.issn.1002-137X.2019.07.025

• Artificial Intelligence • Previous Articles     Next Articles

Feature Selection Algorithm Based on Rough Sets and Fruit Fly Optimization

FANG Bo,CHEN Hong-mei,WANG Sheng-wu   

  1. (School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China)
    (Key Laboratory of Cloud Computing and Intelligent Technology,Southwest Jiaotong University,Chengdu 611756,China)
  • Received:2018-06-21 Online:2019-07-15 Published:2019-07-15

Abstract: Feature selection is one of the most important data preprocessing steps in the field of pattern recognition,aiming at searching the most effective subset with the best value of evaluation function from original data set.This paper proposed a new feature selection strategy based on the rough set theory and fruit fly optimization algorithm.The novel double strategies evolutionary fruit fly optimization algorithm(DSEFOA) is used to search feature subset and execute the iterative optimization.Specially,the selected feature subset is evaluated by the fitness function constructed by attribute dependency and attribute importance simulataneously,which aims at searching important features as many as possible in feature space and further selecting effective feature subset with the most contribution to the decision.Experimental results on UCI datasets show that the proposedfeature selection method can effectively search the feature subset with the minimum information loss and achieve high classification accuracy.

Key words: Attribute dependency, Attribute importance, Double strategies evolutionary, Fruit fly optimization algorithm, Rough sets

CLC Number: 

  • TP301.6
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