Feature Subset Selection using Rough Sets for High Dimensional Data

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Feature Selection (FS) is applied to reduce the number of features in many applications where data has multiple features. FS is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing the irrelevant (and the redundant) features. It has been effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. The removal of irrelevant and redundant information often improves the performance of machine learning algorithms. FS techniques aim at reducing the number of unnecessary features in classification rules. The features are measured by their necessity in heuristic FS techniques. The proposed framework uses filter method to remove irrelevant features, clustering-based method to remove redundant features and Rough Set Theory (RST) with greedy heuristics for feature subset selection.