.png){kind=icon}
Return
Twin support vector quantile regression
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2023.121239.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Twin support vector regression
Quantile regression
Heterogeneity
Asymmetry
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
ShareSaveA new algorithm for support vector regression with automatic selection of hyperparameters.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAIShareSaveCharacterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densitiesGENOME RESEARCH
OAAI
GENOME RESEARCHIF5.5
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more