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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제30권 제3호
- 발행연도
- 2023.5
- 수록면
- 245 - 258 (14page)
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초록· 키워드
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This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y<SUB>i</SUB> ∈ {-1, 1}. The modification takes into account the position of each data item x<SUB>i</SUB> from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and reallife COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.
목차
Abstract
1. Introduction
2. Method
3. Results
4. Discussion
5. Conclusions
References
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