Consistent feature selection reduction about classification data set
Xinling Wu · Computer Engineering and Applications Journal · 2007
The disaccords and the redundancy features of a sample dataset will drop the classification quality and efficiency. In this paper,the method called consistent feature selection reduction is proposed about the classification data set.This method group together the inconsistent datum of the best possible category and make the data set uniform based on the Bayesian formula and a threshold value.Then a category distinguish matrix is built upon the consistent data set and the least feature variable subset that can distinguish the classification accurately is obtained through the category distinguish matrix.A heuristic search strategy and a practical example are given.The result shows the consistent feature selection reduction method can eliminate the disaccords of the sample dataset,select the optimal feature variables,drop the dimension of the data and reduce the redundancy information effectively.