Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 18, Issue 4, Pages 726-744Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2010.2047947
Keywords
Classification; feature selection; feature weighting; fuzzy-optimization theory; margin-maximization principle; Relief algorithm
Funding
- Hong Kong Polytechnic University [Z-08R]
- National Natural Science Foundation of China [60773206, 60903100, 60975027, 90820002]
- Natural Science Foundation of Jiangsu province [BK2009067]
- Doctoral Foundation of Jiangnan University
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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.
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