A Multiobjective Multiclass Support Vector Machine Restricting Classifier Candidates Based on k-Means Clustering

In this paper, we propose a reduction method for the multiobjective multiclass support vector machine (MMSVM) which can maintain the discrimination ability and reduce the computational complexity of the original MMSVM. The proposed method finds some centroids of each class by a k-means clustering and obtains a classifier based on the centroids where the normal vectors of the corresponding separating hyperplanes are given by weighted sums of the centroids, while the geometric margins are exactly maximized between class pairs. Through some numerical experiments for benchmark problems, we observed that the proposed method can reduce the computational complexity without decreasing its generalization ability widely.