Abstract Most existing feature selection algorithms usually select only one feature randomly from the highly correlated feature subset with great contribution to classification,which results in the degradation of data readability and classification performance. To overcome the problem, a multi-class feature selection algorithm based on support vector machine(MFSSVM)is proposed. The proposed feature selection algorithm permits highly correlated features to be selected or removed together, and it allows dimension reduction while obtaining effective features. The experimental results on both simulated datasets and benchmark datasets illustrate the feasibility and effectiveness of the feature set selected by MFSSVM.
2014, Vol. 27 
