抽象的な

Feature subset selection using filtering with Mutual information and Maximal information coefficient

C.Gayathri

Feature subset selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Current existing algorithms for feature sub set selection works only based on conducting statistical test like Pearson test or symmetric uncertainty test to find the correlation between the features and apply threshold to filter redundant and irrelevant features. FAST algorithm uses symmetric uncertainty test for feature subset selection. In this work I extend the FAST algorithm by applying the Mutual information and maximal information coefficient to improve the efficiency and effectiveness of the feature subset selection.