Minimum Coverage Hypersphere Based Category-Separability Criterion and Feature Selection

There is still a problem, lack of enough generalization ability, with existing feature selection methods. To solve this problem, a supervised feature selection method base on support vector machine is proposed in view of generalization ability of support vector machine for small sample set and ability of processing high-dimensional data of kernel function. The new method introduces the category-separability criterion in terms of minimum coverage hypersphere of samples, and uses the criterion as the feature assessment index to feature sorting and feature selection. The experimental results show that this method can obtain a reasonable feature sorting, eliminate unrelated feature in the data set effectively.