Dynamic Classifier Ensemble Selection Based on GMDH

摘要：

Dynamic classifier selection (DCS) plays a strategic role in the field of multiple classifiers system. This article introduces group method of data handing (GMDH) theory to DCS, and presents a novel strategy GAES for adaptive classifier ensemble selection first. Then it extends this algorithm and proposes dynamic classifier ensemble selection based on GMDH (GDES). For each test pattern, GDES is able to select an appropriate ensemble from the classifier pool adoptively, determine the combination weights among base classifiers, and complete the combination process automatically. We experimentally test GDES over 6 UCJ data sets. The results clearly show that, GDES outperforms the fusion method MAJ (Xu et al., 1992) and also performs slightly better than DCS-LCA (Woods et al, 1997) and KNORA (Ko et al., 2008).

作者: Jin Xiao Changzheng He

作者单位: Business School Sichuan University Chengdu 610064, P.R.China

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 731-734

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）