关键词: 序贯最小优化, 双边加权模糊支持向量机, 支持向量机, 模糊支持向量

Abstract: High computational complexity limits the applications of the Bilateral-Weighted Fuzzy Support Vector Machine (BW-FSVM) model in practical classification problems. In this paper, the Sequential Minimal Optimization (SMO) algorithm,which firstly decomposed the overall Quadratic Program (QP) problem into the smallest possible QP sub-problems and then solved these QP sub-problems analytically, was proposed to reduce the computational complexity of the BW-FSVM model. A set of experiments were conducted on three real world benchmarking datasets and two artificial datasets to test the performance of the SMO algorithm. The results indicate that compared with the traditional interior point algorithm, the SMO algorithm can reduce significantly the computational complexity of the BW-FSVM model without influencing the testing accuracy, and makes it possible for the BW-FSVM model to be applied to practical classification problems with outliers or noises.

Key words: Sequential Minimal Optimization (SMO), Bilateral-weighted fuzzy support vector machine, Support Vector Machine (SVM), Fuzzy Support Vector Machine (FSVM)