A feature subset selection method based on symmetric uncertainty and Ant Colony Optimization

Feature subset selection is one of the key problems in the area of pattern recognition and machine learning. Feature subset selection refers to the problem of selecting only those features that are useful in predicting a target concept i.e. class. Data acquired through different sources are not particularly screened for any specific task e.g. classification, clustering, anomaly detection, etc. When the data are fed to a learning algorithm, its results deteriorate. The proposed method is a pure filter based feature subset selection technique which incurs less computational cost and highly efficient in terms of classification accuracy. Moreover, along with high accuracy the proposed method requires less number of features in most of the cases. In the proposed method the issue of feature ranking and threshold value selection is addressed. The proposed method adaptively selects number of features as per the worth of an individual feature in the dataset. An extensive experimentation is performed, comprised of a number of benchmark datasets over three well known classification algorithms. Empirical results endorse efficiency and effectiveness of the proposed method.