Twin support vector quantile regression
Yafen Ye Zhihu Xu Jinhua Zhang Weijie Chen Yuanhai Shao
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 artiffcial data sets, ffve 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 efffciency of the learning process.