Twin support vector quantile regression

We propose a twin support vector quantile regression (TSVQR) to capture the heterogeneous and asymmetric information in modern data. Using a quantile parameter, TSVQR effectively depicts the heterogeneous distribution information with respect to all portions of data points. Correspondingly, TSVQR constructs two smaller sized quadratic programming problems (QPPs) to generate two nonparallel planes to measure the distributional asymmetry between the lower and upper bounds at each quantile level. The QPPs in TSVQR are smaller and easier to solve than those in previous quantile regression methods. Moreover, the dual coordinate descent algorithm for TSVQR also accelerates the training speed. Experimental results on six artificial data sets, five benchmark data sets, two large scale data sets, two time-series data sets, and two imbalanced data sets indicate that the TSVQR outperforms previous quantile regression methods in terms of the effectiveness of completely capturing the heterogeneous and asymmetric information and the efficiency of the learning process.

Automated summary

This paper proposes a twin support vector quantile regression (TSVQR) method to capture the heterogeneous and asymmetric information in modern data. TSVQR effectively depicts the heterogeneous distribution information with respect to all portions of data points using a quantile parameter. The method constructs two smaller sized quadratic programming problems to measure the distributional asymmetry between the lower and upper bounds at each quantile level. Experimental results show that TSVQR outperforms previous quantile regression methods in terms of capturing heterogeneous and asymmetric information effectively and efficiently in various datasets.