A Probabilistic Approach to Feature Selection - A Filter Solution

Feature selection can be de ned as a problem of nding a minimum set of M relevant at tributes that describes the dataset as well as the original N attributes do where M N After examining the problems with both the exhaustive and the heuristic approach to fea ture selection this paper proposes a proba bilistic approach The theoretic analysis and the experimental study show that the pro posed approach is simple to implement and guaranteed to nd the optimal if resources permit It is also fast in obtaining results and e ective in selecting features that im prove the performance of a learning algo rithm An on site application involving huge datasets has been conducted independently It proves the e ectiveness and scalability of the proposed algorithm Discussed also are various aspects and applications of this fea ture selection algorithm