ORPSW: A New Classifier for Gene Expression Data Based on Optimal Risk and Preventive Patterns
Optimal risk and preventive patterns are itemsets which can identify characteristics of cohorts of individuals who have significantly disproportionate representation in the abnormal and normal groups. In this paper, the authors propose a new classifier namely ORPSW (Optimal Risk and Preventive Sets with Weights) to classify gene expression data based on optimal risk and preventive patterns. The proposed method has been tested on four bench-mark gene expression data sets to compare with three state-of-the-art classifiers: C4.5, Naive Bayes and SVM. The experiments show that ORPSW classifier is more accurate than C4.5 and Naive Bayes classifiers in general, and is comparable with SVM classifier.