A new LogitBoost algorithm for multiclass unbalanced data classification

摘要：

LogitBoost algorithm is an extension of Adaboost algorithm. It replaces the exponential loss of Adaboost algorithm to conditional Bernoulli likelihood loss. LogitBoost-J algorithm further extends the LogitBoost to multiclass situation. But like LogitBoost algorithm and Adaboost algorithm, LogitBoost-J algorithm is not suitable for unbalanced data classification. This paper proposes a new LogitBoost algorithm for multiclass unbalanced data classification. The experiment on practical data shows that this new algorithm performs better than LogitBoost-J algorithm and is competitive to BABoost algorithm.

关键词： Multiclass LogitBoost Unbalanced data

作者: Jie Song Xiaoling Lu Miao Liu Xizhi Wu

作者单位: School of Statistics, Capital University of Economics and Business, Beijing China Center for Applied Statistics, Renmin University of China, Beijing, China School of Statistics, Renm School of Statistics, Central University of Finance and Economics, Beijing, China

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: 1014-1017

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）