A feature selection method based on modified binary coded ant colony optimization algorithm

被引:127
作者
Wan, Youchuan [1 ]
Wang, Mingwei [1 ]
Ye, Zhiwei [2 ]
Lai, Xudong [1 ]
机构
[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
[2] Hubei Univ Technol, Sch Comp Sci, Wuhan 430068, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature selection; Genetic algorithm; Modified binary coded ant colony optimization; Visibility density model; Pheromone density model; PARTICLE SWARM OPTIMIZATION; UNSUPERVISED FEATURE-SELECTION; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; SVM; CLASSIFICATION; DESIGN; ACO;
D O I
10.1016/j.asoc.2016.08.011
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:248 / 258
页数:11
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