A Two-Stage Hybrid Approach for Feature Selection in Microarray Analysis

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.