If-SVM: Iterative factoring support vector machine

Support Vector Machine (SVM) is widely applied in classification and regression tasks where support vectors are pursued through convex quadratic programming technique due to its effectiveness and efficiency. However, existing studies ignore the importance of training samples when they are fed into the model. In this paper, we propose a novel Iterative Factoring Support Vector Machine (If-SVM) method. Sample factoring is introduced in our proposed model to measure the significance of each data point, where it can effectively reduce the negative impact of trivial or noisy data points. In this way, our proposed model is concentrates on the critical data points falling around the hyperplane. By introducing this iterative factoring of data points into SVM, the classification accuracy of our proposed method is above that of 1.45% than other comparative methods in image recognition datasets. Experimental results on a variety of UCI demonstrate that, our proposed method has superior performances in decreasing the total number of support vectors than the other state-of-the-art SVM methods. More importantly, our further experiments also illustrate that, the classification performance of the state-of-the-art SVM methods can be improved 1.29% by incorporating our sample factoring idea into their models, which demonstrate our idea is a useful tool to improve the state-of-art SVM models.