Feature Subset Selection by Using Sorted Feature Relevance

Real world classification applications usually have many features. This increases the complexity of the classification task. Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge. We present a new feature subset selection method by using sorted feature relevance. We tested the new method on real world and artificial datasets and compared its results with existing methods. We showed that the new method chooses good subsets by searching fewer states than the existing methods. In the new method, we first sort the features according to their relevance and test the subsets formed by the most relevant features to find a starting subset for searching the subset space. We show that this technique speeds up the search considerably for most of the problem domains.