Feature Selection and Classification - A Probabilistic Wrapper Approach

Feature selection is de ned as a problem to nd a minimum set of M features for an inductive al gorithm to achieve the highest predictive accuracy from the data described by the original N features where M N A probabilistic wrapper model is proposed as another method besides the exhaus tive search and the heuristic approach The aim of this model is to avoid local minima and exhaustive search The highest predictive accuracy is the crite rion in search of the smallest M Analysis and ex periments show that this model can e ectively nd relevant features and remove irrelevant ones in the context of improving the predictive accuracy of an induction algorithm It is simple straightforward and providing fast solutions while searching for the optimal The applications of such a model its future work and some related issues are also discussed