Feature Selection via Maximizing Fuzzy Dependency

Article type: Research Article

Authors: Hu, Qinghua | Zhu, Pengfei | Liu, Jinfu | Yang, Yongbin | Yu, Daren

Affiliations: Harbin Institute of Technology Harbin 150001, China. E-mail: huqinghua@hit.edu.cn

Abstract: Feature selection is an important preprocessing step in pattern analysis and machine learning. The key issue in feature selection is to evaluate quality of candidate features. In this work, we introduce a weighted distance learning algorithm for feature selection via maximizing fuzzy dependency. We maximize fuzzy dependency between features and decision by distance learning and then evaluate the quality of features with the learned weight vector. The features deriving great weights are considered to be useful for classification learning. We test the proposed technique with some classical methods and the experimental results show the proposed algorithm is effective.

Keywords: feature selection, distance learning, fuzzy rough sets, fuzzy dependency

DOI: 10.3233/FI-2010-222

Journal: Fundamenta Informaticae, vol. 98, no. 2-3, pp. 167-181, 2010