Estimation of Discriminative Feature Subset Using Community Modularity.
Feature selection (FS) is an important preprocessing step in machine learning and data mining. In this paper, a new feature subset evaluation method is proposed by constructing a sample graph (SG) in different k-features and applying community modularity to select highly informative features as a group. However, these features may not be relevant as an individual. Furthermore, relevant in-dependency rather than irrelevant redundancy among the selected features is effectively measured with the community modularity Q value of the sample graph in the k-features. An efficient FS method called k-features sample graph feature selection is presented. A key property of this approach is that the discriminative cues of a feature subset with the maximum relevant in-dependency among features can be accurately determined. This community modularity-based method is then verified with the theory of k-means cluster. Compared with other state-of-the-art methods, the proposed approach is more effective, as verified by the results of several experiments.
School of Mathematics and Physics, Shanghai Dian Ji University, Shanghai 201306, P. R. China.
Guodong
G
Zhao
School of Mathematics and Physics, Shanghai Dian Ji University, Shanghai 201306, P. R. China.
Sanming
S
Liu