Experimental feature selection using the wrapper approach

Machine learning methods provide algorithms for mining databases in order to help analyze the information, find patterns, and improve prediction accuracy. In practice, the user of a data mining tool is interested in accuracy, efficiency, and comprehensibility for a specific domain which may be reached through feature selection. In this work we use the wrapper approach for Feature Subset Selection. The FSS algorithm from MLC++ library was used to run experiments with datasets containing many features. Accuracies for five inducers using all features, features found by FSS as well as the union of all those selected features are presented. Results confirm the superiority of FSS wrapper approach but in some cases the computational cost is excessive.