Multiobjective Support Vector Machines: Handling Class Imbalance With Pareto Optimality.

Abstract

Support vector machines (SVMs) seek to optimize three distinct objectives: maximization of margin, minimization of regularization from the positive class, and minimization of regularization from the negative class. The right choice of weightage for each of these objectives is critical to the quality of the classifier learned, especially in case of the class imbalanced data sets. Therefore, costly parameter tuning has to be undertaken to find a set of suitable relative weights. In this brief, we propose to train SVMs, on two-class as well as multiclass data sets, in a multiobjective optimization framework called radial boundary intersection to overcome this shortcoming. The experimental results suggest that the radial boundary intersection-based scheme is indeed effective in finding the best tradeoff among the objectives compared with parameter-tuning schemes.