Bisecting Data Partitioning Methods for Min-Max Modular Support Vector Machine

摘要：

Min-Max Modular Support Vector Machines (M3-SVM) is a well-known ensemble learning method. One of key problems for M3-SVM is to find a quick and effective method for data partition. This paper presents a new data partitioning method-BEK, which is based on the Bisecting K-means clustering with equalization function. BEK generally can get global optimal solution with low time complexity, and more importantly, it can obtain the relatively balanced partitions, which are very important for M3-SVM to deal with huge data. Experimental results on realworld data sets show that this bisecting partitioning method can effectively improve the classification performance of M3-SVM without increasing its time cost

作者: Xiao-Min Xie Yun Li

作者单位: College of Computer, Institute of Computer Technology Nanjing University of Posts and Telecommunications Nanjing, China, 210003

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1032-1036

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）