ROBUSTNESS ANALYSIS OF DIVERSIFIED ENSEMBLE DECISION TREE ALGORITHMS FOR MICROARRAY DATA CLASSIFICATION

摘要：

Ensemble classification methods have shown promise for achieving higher classification accuracy for Microarray data classification analysis. As noise values do exist in all Microarray data even after Mic roar ray data preprocessing stage, robustness is therefore another very important criteria in addition to accuracy for evaluating reliable Microarray classification algorithms. In this paper, we conduct experimental comparison of our newly developed MDMT with C4.5, BaggingC4.5, Ad-aBoostingC4.5, Random Forest and CS4 on four Microarray cancer data sets. We test and evaluate how well a given single or ensemble classifier can tolerate noise data in unseen test datasets, particularly with increasing levels of noise. The experimental results show that MDMT tolerates the noise values in unseen test data sets better than other compared methods do, particularly with increasing levels of noise data. We observe that a Random forests is comparable to MDMT in term of resistance to noise. The experimental results also show that ensemble decision tree methods tolerate the noise values better than single tree C4.5 does. We conclude that avoiding overlapping genes exist among the ensemble trees is an intuitive, simple and effective way to achieve higher degree of diversity for ensemble decision tree methods. The algorithm based on this principal is more reliable to deal with Microarray data sets with certain level of noise data.

关键词： Robustness Microarry data Ensemble decision trees Classification Diversity

作者: HONG HU JIU-YONG LI HUA WANG GRANT DAGGARD LI-ZHEN WANG

作者单位: Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, QLD 4350 Aust School of Computer and Information Science, University of South Australia, Mawson Lakes, Adelaide, S Department of Biological and Physical Sciences, University of Southern Queensland, Toowoomba, QLD 43 Department of Computer Science and Engineering, School of Information, Yunnan University, Kunming, 6

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: 115-120

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）