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학술논문

Robust least squares support vector machine with the absolute error function

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영문명
발행기관
한국자료분석학회
저자명
Kang-Mo Jung
간행물 정보
『Journal of The Korean Data Analysis Society (JKDAS)』Vol.26 No.6, 1661~1670쪽, 전체 10쪽
주제분류
자연과학 > 통계학
파일형태
PDF
발행일자
2024.12.31
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국문 초록

This paper presents a novel approach to improve the robustness of the least squares support vector machine (LS-SVM) for classification tasks by addressing its sensitivity to outliers in support vectors. While LS-SVM is computationally efficient due to its reliance on matrix operations, its use of a squared error loss function makes it highly sensitive to outliers, and it typically requires all data points as support vectors, which reduces model interpretability and efficiency. To overcome these limitations, we propose a LS-SVM variant that employs an absolute error loss function, which reduces the influence of outliers on the decision boundary. However, optimizing the absolute error function poses challenges due to its non-smooth nature. To address this, we apply the split-Bregman iterative method, which efficiently handles non-differentiable optimization problems by decomposing them into sub-problems that converge rapidly. Our method’s performance is evaluated through experiments on benchmark datasets, comparing it to LS-SVM and weighted LS-SVM in terms of classification accuracy, robustness to noise. Results indicate that the proposed model outperforms LS-SVM and weighted LS-SVM by maintaining accuracy in the presence of outliers, leading to improved model interpretability and efficiency.

영문 초록

목차

1. Introduction
2. Least squares support vector classifier
3. The absolute error loss function
4. Numerical experiments
5. Concluding Remarks
References

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APA

Kang-Mo Jung. (2024).Robust least squares support vector machine with the absolute error function. Journal of The Korean Data Analysis Society (JKDAS), 26 (6), 1661-1670

MLA

Kang-Mo Jung. "Robust least squares support vector machine with the absolute error function." Journal of The Korean Data Analysis Society (JKDAS), 26.6(2024): 1661-1670

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