Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Jianli Shao, Xin Liu and Wenqing He
Additional contact information
Jianli Shao: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
Xin Liu: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
Wenqing He: Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON N6A 3K7, Canada

Mathematics, 2021, vol. 9, issue 9, 1-15

Abstract: Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F -score and G -means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.

Keywords: classification; data-adaptive kernel functions; image data; multi-category classifier; predictive models; support vector machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/9/936/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/9/936/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:9:p:936-:d:541779

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().