Effective feature selection using feature vector graph for classification
Optimal feature subset selection is often required as a preliminary work in machine learning and data mining. The choice of feature subset determines the classification accuracy. It is a crucial aspect to construct efficient feature selection algorithm. Here, by constructing the feature vector graph, a new feature evaluation criterion based on community modularity in complex network is proposed to select the most informative features. To eliminate the relevant redundancy among features, conditional mutual information-based criterion is used to capture information about relevant independency between features, which is the amount of information they can predict about label variable, but they do not share. The most informative features with maximum relevant independency are added to the optimal subset. Integrating these two points, a method named the community modularity Q value-based feature selection (CMQFS) is put forward in this paper. Furthermore, our method based on community modularity can be certified by k-means cluster theory. We compared the proposed algorithm with other state-of-the-art methods by several experiments to indicate that CMQFS is more efficient and accurate.
feature selection feature vector graph classification accuracy community modularity relevant independency