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논문 기본 정보

자료유형
학술저널
저자정보
Chong Sun Hong (Sungkyunkwan University) Sol Mi Park (Sungkyunkwan University)
저널정보
한국데이터정보과학회 한국데이터정보과학회지 한국데이터정보과학회지 제29권 제5호
발행연도
2018.9
수록면
1,319 - 1,328 (10page)
DOI
10.7465/jkdi.2018.29.5.1319

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초록· 키워드

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In the process of evaluating the performance of the classification model for two distribution functions based on the cost function, unreliable samples were rejected based on the ROC-based reject rule due to the high cost of wrong classification in order to minimize classification errors in the classification process. In this paper, we consider rejected samples between the two rejection thresholds obtained by the ROC-based reject rule as the unresolved, so we propose a method for estimating the optimal threshold by setting a new cost function. In defining the probability density functions for the unresolved, various weights are given only to the cost of the misclassified FN and FP in order to reset the cost function for the unresolved. The changes in cost corresponding to the optimal threshold minimizing the cost function are then examined. For the samples between the two rejection thresholds, which are considered to be the unresolved, the new cost functions are divided into the cases where the costs of FN and FP are the same and the cases where the costs are different. In the cost of the unresolved, the results differ according to changes in the misclassification cost. However, the optimal threshold of the unresolved is similar to that of the estimated optimal threshold with respect to various weights of the misclassifications.

목차

Abstract
1. Introduction
2. Unresolved inference using ROC-based reject rule
3. Illustrative Examples
4. Simulation results
5. Conclusion
References

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