An Improved Weighted Posterior Probability Reconstruction Strategy for Multi-class Support Vector Machine

摘要：

After analysis of the existing one-versus-one (OVO) decomposition methods for multi-class support vector machine (SVM), the improved weighted posterior probability (IWPP) reconstruction strategy is presented to combine binary SVM-based classifiers to multi-class one. The new strategy can resolve the unclassifiable region problems in the conventional max-win-voting (MWV) method and increase recognition accuracy. Firstly, the different prior probabilities of these binary SVM-based classifiers in OVO decomposition are considered. Then, the improved weight coefficients for combination of the probability output among these binary classifiers are re-derived based on conditional probability theory. To validate the proposed strategy, four examples on UCI database are used to illustrate the logical correctness of IWPP. Tests show that the IWPP strategy has less classification errors, better classification ability and more stable probability output than the original ones.

关键词： Multi-class support vector machine Posterior probability Reconstruction Strategy

作者: Xiaohong Wang

作者单位: Key Laboratory of Numerical Control of Jiangxi Province, Jiujiang University, Jiujiang 332005, China School of Electronic and Information Engineering; Dalian University of Technology, Dalian, 116024, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 5898-5903

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）