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Journal Article
Interpretable Support Vector Machine and Its Application to Rehabilitation Assessment
- Authors
- Woojin Kim, Hyunwoo Joe, Hyun-Suk Kim, Daesub Yoon
- Issue Date
- 2024-09
- Citation
- ELECTRONICS, v.13, no.18, pp.1-14
- ISSN
- 2079-9292
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- This paper does present an interpretable support vector machine (SVM) and its application to rehabilitation assessment. We introduce the concept of nearest boundary point to standardize the one-class SVM decision function and determine the shortest path for data from abnormal cases to become those from normal cases. This analytical approach is computationally simple and provides a unique solution. The nearest boundary point of abnormal data can also be used to analyze the cause of abnormal classification and indicate countermeasures for normalization. These properties render the proposed interpretable SVM valuable for medical assessment applications and other problems that require careful consideration of classification results for treatment. Simulation and application results demonstrate the feasibility and effectiveness of the proposed method.
- KSP Keywords
- Analytical Approach, Boundary points, Medical assessment, Support VectorMachine(SVM), Unique solution, abnormal data, decision function, one-class svm, shortest path, simulation applications, vector machine(LSSVM)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
