EXPERIMENTAL COMPARISON BETWEEN IMPLICIT AND EXPLICIT MCSS CONSTRUCTION METHODS

摘要：

Multiple Classifier Machines (MCSs) is a very popular research topic in recent years. It has been proved theoretically and empirically to outperform single classifiers in many scenarios. Creating diverse sets of classifier is one of the key issues in MCSs. One kind of method measures the diversity among the individual classifier when building the MCS while the other method does not consider the diversity value directly.This paper compared these two kinds of methods experimentally. From the experiments, the performances of implicit and explicit methods are very close. We can conclude that it is not necessary to consider the diversity measure among individual classifiers directly for building a good MCS.

关键词： Multiple Classifier Machines (MCSs) ensemble diversity

作者: PATRICK P.K.CHAN AKI P.F.CHAN ERIC C.C.TSANG DANIEL S.YEUNG

作者单位: Department of computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2218-2221

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）