Support vector machine imbalanced data classification based on weighted clustering centroid
Yong Zhong · Caai Transactions on Intelligent Systems · 2013
Classification of imbalanced data has become a research hot topic in machine learning.Traditional classification algorithms assume that different classes have balanced distribution or equal misclassification cost,thus,making it hard to get ideal result of classifications.A support vector machine(SVM) classification method based on weighted clustering centroid was proposed in this paper.First,unsupervised clustering was applied to the positive and negative samples respectively to extract the clustering centroid of each clustering,which was represented the most in compactness of the clustering sample.Next,all clustering centroids formed a new set of balance training.In order to minimize the information loss during clustering,each clustering centroid was associated with a weight factor that was defined proportional to the number of samples of the class.Finally,all clustering centroids and weight factors participated in the training of the improved SVM model.Experimental results show that the proposed method can make the sample selected from model train sets more typical and improve the classification performance better than other sampling techniques for dealing with imbalanced data.