Integration of graph clustering with ant colony optimization for feature selection

被引:128
|
作者
Moradi, Parham [1 ]
Rostami, Mehrdad [1 ]
机构
[1] Univ Kurdistan, Dept Comp Engn, Sanandaj, Iran
关键词
Feature selection; Ant colony optimization; Filter method; Graph clustering; FEATURE SUBSET-SELECTION; UNSUPERVISED FEATURE-SELECTION; PARTICLE SWARM OPTIMIZATION; HYBRID GENETIC ALGORITHM; INFORMATION GAIN; IMAGE RETRIEVAL; CLASSIFICATION; SEARCH; REDUCTION; PSO;
D O I
10.1016/j.knosys.2015.04.007
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 an important preprocessing step in machine learning and pattern recognition. The ultimate goal of feature selection is to select a feature subset from the original feature set to increase the performance of learning algorithms. In this paper a novel feature selection method based on the graph clustering approach and ant colony optimization is proposed for classification problems. The proposed method's algorithm works in three steps. In the first step, the entire feature set is represented as a graph. In the second step, the features are divided into several clusters using a community detection algorithm and finally in the third step, a novel search strategy based on the ant colony optimization is developed to select the final subset of features. Moreover the selected subset of each ant is evaluated using a supervised filter based method called novel separability index. Thus the proposed method does not need any learning model and can be classified as a filter based feature selection method. The proposed method integrates the community detection algorithm with a modified ant colony based search process for the feature selection problem. Furthermore, the sizes of the constructed subsets of each ant and also size of the final feature subset are determined automatically. The performance of the proposed method has been compared to those of the state-of-the-art filter and wrapper based feature selection methods on ten benchmark classification problems. The results show that our method has produced consistently better classification accuracies. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:144 / 161
页数:18
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