A Novel Ant Colony Optimization Approach to Feature Selection Based on Fuzzy Entropy

Author

Li, Xiang ; Xi, Haibo ; Lin, Heping

Author_Institution

Sch. of Comput. Sci., Northeast Normal Univ., Changchun, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Feature selection is a most important procedure which can affect the performance of pattern recognition systems. Since most feature selection algorithms easily fall into local optimum, a novel ant colony optimization approach to feature selection based on fuzzy entropy is proposed (ACOFE). In the proposed algorithm, fuzzy entropy is adopted as pheromone information for ant colony optimization. In order to verify the proposed approach, datasets in UCI Machine Learning Repository are used to test the performance. Simulation experiment results demonstrate that this approach provides higher classification accuracy.

Keywords

entropy; fuzzy set theory; learning (artificial intelligence); optimisation; pattern recognition; UCI machine learning repository; ant colony optimization; feature selection; fuzzy entropy; pattern recognition systems; Ant colony optimization; Computer science; Entropy; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Machine learning; Machine learning algorithms; Pattern recognition; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5365508

Filename

5365508