A NEW MEASURE OF CLASSIFIER DIVERSITY IN MULITIPLE CLASSIFIER SYSTEM

摘要：

Diversity among the team has been recognized as a very important characteristic in classifier combination. There are varied diversity measures. They can be categorized into two types, painvise diversity measures and non-pairwise diversity measures. Above diversity measures are defined based on oracle outputs of classifier. While using diversity measures to calculate diversity of classifiers that have soft label outputs, much information about class will be lost. That is a weakness of above measures. In order to solve the problem, this paper puts forward a new diversity measure, which can be used in the classifiers that have soft label outputs. Experimental results show that it contains more information about classifier outputs and accurately reflects the difference of classifier outputs.

关键词： MCS Diversity measures Oracle output Soft label output

作者: TIE-GANG FAN YING ZHU JUN-MIN CHEN

作者单位: Faculty of Mathematics and Computer Science, Hebei University, Baoding,Hebei, China College of Science, Hebei Agriculture University, Baoding,Hebei, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 18-21

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）