Multi-hyperplane twin support vector regression guided with fuzzy clustering

Abstract

In recent years, twin support vector regression has become a hot research topic because of its low computing time and excellent performance. It can be observed, however, that either the support vector regression or twin support vector regression have no more than two regression hyperplanes. Many research studies have ignored the potential of multiple hyperplanes regression algorithms. In this paper, a one-versus-all twin support vector regression (OVATWSVR) with multiple regression hyperplanes is proposed, in order to achieve excellent regression performance though multi-hyperplane structure. Suppose that the input data implicitly has p categories, OVATWSVR solves a smaller quadratic programming problem (QPP) and repeats this process p times, resulting in p regression hyperplanes. For the purpose of mining the implicit category information of each point to assist OVATWSVR in training hyperplanes, meanwhile considering the fuzzy characteristics of points (they lack classification labels) in different types of regression datasets, we further propose a fuzzy clustering algorithm, namely fuzzy weighted K-nearest neighbors fuzzy density peak clustering (FKNN-FDPC), to provide OVATWSVR with information regarding the category of each point. A fuzzy membership function, also guided by FKNN-FDPC, is added to OVATWSVR in order to enhance the capability of OVATWSVR to handle possible fuzzy properties in data, thus creating FOVATWSVR. F3OVATWSVR is a reasonable name for the entire multiple phases’ algorithm. Several UCI benchmark datasets, a real-world competition dataset and a state of health (SOH) estimation of lithium-ion batteries dataset are used to verify the superiority and effectiveness of F3OVATWSVR.