Feature selection based on bayes minimum error probability

Feature selection is very important to classification. In this paper, we propose to select features based on Bayes minimum error probability (SFBMEP). And we exploit the proposed method to sift possible functional genes for classifying cancers. The method dynamically evaluates all available genes and sifts only one gene at a time with the computation of minimum error rate. A gene is selected into the feature subset if it combines with the selected genes in a feature vector and these combined features can gain better classification information. Based upon the method, the classifier is constructed accordingly. Compared with some other machine learning methods, the experimental results show that the classifiers induced based on the proposed method are capable of generating accurate and interpretable results in biomedical applications.