Feature subset selection algorithm based on symmetric uncertainty and interaction factor

Since either only one metric is employed or two metrics are adopted and each of them is compared separately to measure redundant features, several existing feature subset selection algorithms cannot obtain the desired performance. To address the problem, a feature subset selection algorithm named symmetric uncertainty and interaction factor (SUIF) is presented. SUIF first exploits symmetric uncertainty to evaluate relevant features and removes irrelevant features. Then, it uses a graph theoretic representation to process these relevant features and removes some edges whose weights are some smaller values. Following that, it adopts Louvain community detection algorithm to cluster features into several clusters. Finally, it utilizes two metrics, symmetric uncertainty and interaction factor, and the method of equal interval division and ranking to evaluate features in each cluster, and removes redundant features. For validating the performance of SUIF, FCBF, INTERACT, FAST, SAOLA, NFCBF and EID-TII are exploited for comparison. Experimental results indicate that SUIF can obtain better feature selection performance.