Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization

被引:50
作者
Deng, Zhaohong [1 ]
Chung, Fu-Lai [2 ]
Wang, Shitong [1 ]
机构
[1] Jiangnan Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Classification; feature selection; feature weighting; fuzzy-optimization theory; margin-maximization principle; Relief algorithm; GENE-EXPRESSION PROFILES; FEATURE-SELECTION; ALGORITHM; CLASSIFICATION; INFORMATION; PREDICTION; CANCER;
D O I
10.1109/TFUZZ.2010.2047947
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A latest advance in Relief-feature-weighting techniques is that the iterative procedure of Relief can be approximately expressed as a margin maximization problem, and therefore, its distinctive properties can be investigated with the help of optimization theory. Being motivated by this advance, the Relief-feature-weighting algorithm is investigated for the first time within a fuzzy-optimization framework. A new margin-based objective function that incorporates three fuzzy concepts, namely, fuzzy-difference measure, fuzzy-feature weighting, and fuzzy-instance force coefficient, is introduced. By the application of fuzzy optimization to this new margin-based objective function, several useful theoretical results are derived, based upon which, a set of robust Relief-feature-weighting algorithms are proposed for two-class data, multiclass data, and, then, online data. As demonstrated by extensive experiments in synthetic datasets, the University of California at Irvine (UCI)-benchmark datasets, cancer-gene-expression datasets, and face-image datasets, the proposed algorithms were found to be competitive with the state-of-the-art algorithms and robust for datasets with noise and/or outliers.
引用
收藏
页码:726 / 744
页数:19
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