Regression of survival data via twin support vector regression. Issue 9 (27th September 2022)

• Record Type: Journal Article
• Authors: Ma, Guangzhi; Zhao, Xuejing
• Is Part Of: Communications in statistics. Volume 51:Issue 9(2022)
• Page Start: 5126
• Page End: 5138
• Publication Date: 2022-09-27
• Subjects: Machine learning -- Survival analysis -- Survival twin support vector regression -- Support vector regression -- Twin support vector regression; 62N01; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2020.1757710
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: The objective of this paper is to provide a new algorithm for the regression of survival data. We propose an algorithm of Survival twin support vector regression (STWSVR), an extension of Twin support vector regression (TWSVR) in binary classification, to explore the analysis of survival data with right censoring. The main algorithm STWSVR is to solve a pair of quadratic programing problems (QPPs) with some tuning parameters. The performance of the algorithm is compared with the survival SVR (SSVR), Cox proportional hazards regression model, Cox proportional hazards model with lasso regularization(Cox-lasso), random survival forests (RSF) and generic gradient boosting algorithm (L2-boosting) on one simulated dataset and 6 practical clinical datasets, using two evaluation measures, C-index and Logrank χ 2 -statistic.