Biologically Relevant Association Rules For Classification Of Microarray Data

In this paper we investigate a method for classifying microarray data using association rules. Associative classifiers, classification systems based on association rules, show good performance level while being easy to read and understand. This feature is especially attractive for biological data where experts can read and validate the association rules. Relevant features are selected using Support Vector Machines with Recursive Feature Elimination. These features are discretized according to their relative expression levels (upregulated, downregulated or no change) and then they are used to build an associative classifier. The proposed combination proves highly accurate for the studied microarray data collection. In addition, the classification rules discovered and employed in the classification process prove to be biologically relevant.