Markov blanket: Efficient strategy for feature subset selection method for high dimensional microarray cancer datasets

In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection is by using Bayesian network based on Markov blanket. We present and compare the performance of the different approaches of features (genes) subset selection methods based on Wrapper and Markov Blanket models for the five-microarray cancer datasets. The first alternative depends on Memetic algorithms (MAs) for feature selection method. In the second alternative, we use MRMR (Minimum Redundant Maximum Relevant) for feature subset selection method hybridized by genetic search optimization techniques. We compare the performance of Markov blanket model with most common classification algorithms for those set of features. The results show that the performance measures of classification algorithms based on Markov Blanket model mostly offer better accuracy rates than other types of classical classification algorithms for the cancer Microarray datasets.