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. Most of the data acquired through different sources are not particularly screened for any specific task e.g. classification, clustering, anomaly detection, etc. When this data is fed to a learning algorithm, its results deteriorate. The proposed method is a pure filter based feature subset selection technique that 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.