Twin support vector quantile regression
This is an incremental improvement for researchers and practitioners in quantile regression, offering faster training and better performance on heterogeneous data.
The authors tackled the problem of capturing heterogeneous and asymmetric information in data by proposing Twin Support Vector Quantile Regression (TSVQR), which constructs two smaller quadratic programming problems to generate nonparallel planes at each quantile level, resulting in improved effectiveness and efficiency over previous methods across various datasets.
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.