Description

In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Chi Squared, PCA as well as other feature selection. We are implementing classification algorithm like SVM the experimental results shows that some performance metrics such as accuracy and prediction status.