MBSVR: Multiple birth support vector regression

Zichen Zhang, Shifei Ding, Yuting Sun

Abstract: Although the learning speed in twin support vector regression (TWSVR) is four times that in support vector regression (SVR), the computing time and fitting precision of TWSVR are limited. This paper develops multiple birth support vector regression (MBSVR), motivated by the multiple birth support vector machine (MBSVM) formulation. MBSVR constructs the final regressor from K hyperplanes, each of which is obtained by solving a small quadratic programming problem (QPP) with the associated constraints, in which all points in each of the corresponding class should be as far away as possible from its corresponding hyperplane. Since MBSVM can be seen as an extension of the twin support vector machine (TWSVM) and its computing time is less than that of TWSVM, the proposed MBSVR is also faster than TWSVR, especially when the number of classes K is large. To verify the performance of the proposed MBSVR, it is compared with TWSVR, TSVR (another form of twin support vector regression) and SVR on several synthetic datasets and UCI datasets.