A two-stage hybrid approach for feature selection in microarray analysis

摘要：

In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized Receiver Operating Characteristic (ROC) method to eliminate non-significant genes of unapparent variation between normal tissues and tumors. Subsequently, these selected genes are clustered through an unsupervised learning algorithm to reduce overall training samples under the same condition. In addition, the resulting samples have been verified by means of experimenting with the SVM and KNN methods. The experimental results show that our approach has potentials to effectively reduce samples for microarray analysis.

关键词： data mining machine learning microarray Receiver operating characteristic feature selection

作者: Chung-Hong Lee Hsin-Chang Yang Chih-Hong Wu Yi-Chia Lan

作者单位: Dept of Electrical Engineering National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan Dept of Information Management National University of Kaohsiung, Kaohsiung, Taiwan

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-4

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）