Combined outputs framework for twin support vector machines

摘要

Twin support vector machine (TWSVM) is regarded as a milestone in the development of powerful SVMs. However, there are some inconsistencies with TWSVM that can lead to many reasonable modifications with different outputs. In order to obtain better performance, we propose a novel combined outputs framework that combines rational outputs. Based on this framework, an optimal output model, called the linearly combined twin bounded support vector machine (LCTBSVM), is presented. Our LCTBSVM is based on the outputs of several TWSVMs, and produces the optimal output by solving an optimization problem. Furthermore, two heuristic algorithms are suggested in order to solve the optimization problem. Our comprehensive experiments show the superior generalization performance of our LCTBSVM compared with SVM, PSVM, GEPSVM, and some current TWSVMs, thus confirming the value of our theoretical analysis approach.