Incremental Maximum Gaussian Mixture Partition For Classification

摘要：

　　In the field of classification,the main task of most algorithms is to find a perfect decision boundary.However,most decision boundaries are too complex to be discovered directly.Therefore,in this paper,we proposed an Incremental Maximum Gaussian Mixture Partition(IMGMP)algorithm for classification,aiming to solve those problems with complex decision boundaries.As a self-adaptive algorithm,it uses a divide and conquer strategy to calculate out a reasonable decision boundary by step.An Improved K-means clustering and a Maximum Gaussian Mixture model are used in the classifier.This algorithm also has been tested on artificial and real-life datasets in order to evaluate its remarkable flexibility and robustness.

关键词： Classification Gaussian Function K-means

作者: Xianbin Hong Jiehao Zhang Sheng-Uei Guan Di Yao Nian Xue Xuan Zhao Xin Huang

作者单位: Department of Computer Science and Software Engineering Xian Jiaotong-Liverpool University Suzhou,China

会议类型: 国际会议

会议名称: 2017年第2届联合国际信息技术、机械与电子工程国际会议(JIMEC2017)

会议地点: 重庆

会议语种:英文

页码: 141-144

在线出版日期: 2017-10-04（万方平台首次上网日期，不代表论文的发表时间）