Fuzzy relevance vector machine for learning from unbalanced data and noise

Authors:
Ding-Fang Li;Wen-Chao Hu;Wei Xiong;Jin-Bo Yang
Affiliations:
School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 Hubei, PR China;School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 Hubei, PR China;School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 Hubei, PR China;School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 Hubei, PR China
Venue:
Pattern Recognition Letters
Year:
2008

Citing 5
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Abstract

Handing unbalanced data and noise are two important issues in the field of machine learning. This paper proposed a complete framework of fuzzy relevance vector machine by weighting the punishment terms of error in Bayesian inference process of relevance vector machine (RVM). Above problems can be learned within this framework with different kinds of fuzzy membership functions. Experiments on both synthetic data and real world data demonstrate that fuzzy relevance vector machine (FRVM) is effective in dealing with unbalanced data and reducing the effects of noises or outliers.