Improved inputted feature selection classification algorithm based on regular mutual information

For the issue that the traditional feature selection method in determining redundant parameter β based on the Mutual Information(MI), a kind of improved feature selection algorithm for NMIFS-FS2 is proposed. This algorithm input is characterized by a combination of MI and between classes, instead of the traditional algorithms in a single feature MI and between classes when selecting continuous or discrete features, solving the problem that redundancy parameter β is very difficult to determine, and expands the scope of application. Two sets of experiments conducted to verify the validity of this algorithm. Experimental results show that this algorithm, compared to several traditional classification algorithms,has better robustness, stability and efficiency.