Multi-task support vector machine with pinball loss. (November 2021)

• Record Type: Journal Article
• Authors: Zhang, Yunhao; Yu, Jiajun; Dong, Xinyi; Zhong, Ping
• Is Part Of: Engineering applications of artificial intelligence. Volume 106(2021)
• Publication Date: 2021-11
• Subjects: Multi-task learning -- Support vector machine -- Robustness and sensitivity -- Pinball loss; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104458
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: With the boom in machine learning, support vector machine (SVM) is widely employed in pattern recognition. However, most of SVM models concentrate on single-task learning, multi-task learning has been largely neglected. Compared with single-task learning, multi-task learning can achieve a good performance for each task by mining the shared information among tasks. In addition, loss function also plays an important role in the accuracy of SVM. Inspired by multi-task learning and the SVM with pinball loss (pin-SVM), we propose two novel multi-task support vector machines with pinball loss for binary classification, named as MTL-pin-SVM I and MTL-pin-SVM II. Both methods maximize the quantile distance for each task, which realizes less sensitive to noise and more stable for re-sampling. Moreover, MTL-pin-SVM II can use different combinations of kernel functions for different tasks, which can get better performance than other multi-task models by choosing the suitable combinations of kernel functions for different tasks. And they include the multi-task SVM with hinge loss as their special cases, which are denoted as MTL-C-SVM I and MTL-C-SVM II. The extensive experiments on multi-task datasets fully validate the validity of the proposed models. Highlights: Two novel multi-task algorithms named MTL-pin-SVM I and MTL-pin-SVM II are proposed. The proposed methods achieve noise insensitivity and re-sampling stability. MTL-pin-SVM II can utilize different nonlinear transformations for different tasks. The regularization parameter gets a balance between common and private information. Validity is verified by comparing them with related algorithms on benchmark datasets.