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.
Title: Integration of graph clustering with ant colony optimization for featur selection
Paper address：http://www.sciencedirect.com/science/article/pii/S0950705115001458
Sited as: Moradi P., Rostami M.Integration of graph clustering with ant colony optimization for feature selection. Knowledge-Based Systems.2015,84:144-161.DOI: 10.1016/j.knosys.2015.04.007.
The code is in Matlab. A number of functions depend on files in the https://pan.baidu.com/s/1_kX3B61NNw_qI0NWMMfQzg. Ensure that this folder is added to your Matlab path.
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Editor:Xuesen Yang
Institution: Shenzhen University
E-mail:1348825332@qq.com
Edit date:2019-2-27