Fuzzy decision-making SVM with an offset for real-world lopsided data classification

An improved support vector machine (SVM) classifier model for classifying the real-world lopsided data is proposed. The most obvious differences between the model proposed and conventional SVM classifiers are the designs of decision-making functions and the introduction of an offset parameter. With considering about the vagueness of the real-world data sets, a fuzzy decision-making function is designed to take the place of the traditional sign function in the prediction part of SVM classifier. Because of the existence of the interaction and noises influence around the boundary between different clusters, this flexible design of decision-making model which is more similar to the real-world situations can present better performances. In addition, in this paper we mainly discuss an offset parameter introduced to modify the boundary excursion caused by the imbalance of the real-world datasets. Because noises in the real-world can also influence the separation boundary, a weighted harmonic mean (WHM) method is used to modify the offset parameter. Due to these improvements, more robust performances are presented in our simulations.