Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24935
Title: Robust relief-feature weighting, margin maximization, and fuzzy optimization
Authors: Deng, Z
Chung, FL 
Wang, S
Keywords: Classification
feature selection
feature weighting
fuzzy-optimization theory
margin-maximization principle
Relief algorithm
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2010, v. 18, no. 4, 5446326, p. 726-744 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: 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.
URI: http://hdl.handle.net/10397/24935
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2010.2047947
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