Feature selection method Author Name and year of publication Description Drawback
Feature and Relation Selection (FARS) B. Hu, H. Liu, J. He and X. Du (2008) Evaluated by using table symmetrical uncertainty (TSU) which is symmetrical uncertainty (SU) value between relation and class. (over multi-relational dataset) Discrete values are not handled
Feature Selection using InfoDist C. Sha, X. Qiu and A. Zhou (2007) Evaluated by InfoDist which based on information theory. Discrete values are not handled
Wilks Lambda criterion method A. Ouardighi, A. Akadi and D. Aboutajdine (2007) Evaluated by a statistical value used in discriminant analysis. Insufficient to improve the classifier performances only by relevance criterion.
MR2 feature selection method A Unler, A Murat, RB Chinnam (2007) This method uses InfoDist and Pearson Correlation to calculate the relevant features (over multi-relational dataset) Cutoff value is hard to decide
Fast Correlation Based Filter (FCBF) L. Yu and H. Liu(2003) Evaluated by information gain combines optimal subset and feature relevance weight method. Discrete values are not handled
Table 1: Analysis of feature selection method.