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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.9
- 수록면
- 937 - 950 (14page)
- DOI
- 10.7465/jkdi.2020.31.5.937
이용수
초록· 키워드
오류제보하기
Most accuracy measures for finding optimal thresholds on the ROC curve, surface and manifold make use of only misclassification rates or correct classification rates in the confusion matrix. The MADET (maximum absolute determinant) was proposed to use all elements of the confusion matrix. The power of MADET was compared with those of the generalized Youden index and the volume under the ROC surface. In this paper, two and three dimensional MADETs are implemented geometrically. And for the three dimensional confusion matrix, MADET is compared with some accuracy measures defined only as the correct classification rates or misclassification rates. When the standard deviation of one distribution is greater than other two, MADET might be conclude to be as good accurate measure as Youden index, and better than any other measures, based on the sum of correct classification rates which is the trace of the confusion matrix.
목차
Abstract
1. Introduction
2. Geometrical descriptions of MADET
3. Accuracy measures based on correct classification rates
4. Comparison with other accuracy measures
5. Illustrative example
6. Conclusion
References
참고문헌 (21)
참고문헌 신청
Attwood, K. / 2014 / Diagnostic thresholds with three ordinal groups / Journal of Biopharmaceutical Statistics 24:608~633
Bradley, A. P. / 1997 / The use of the area under the ROC curve in the evaluation of machine learning algorithms / Pattern Recognition 30:1145~1159
Connell, F. A. / 1985 / Measures of gain in certainty from a diagnostic test / American Journal of Epidiology 121:744~753
Dodd, L. E. / 2003 / Partial AUC estimation and regression / Biometrics 59:614~623
Dong, T. / 2017 / A new diagnostic accuracy measure and cut-point selection criterion / Statistical methods in medical research 26:2832~2852
Bradley, A. P. / 1997 / The use of the area under the ROC curve in the evaluation of machine learning algorithms / Pattern Recognition 30:1145~1159
Connell, F. A. / 1985 / Measures of gain in certainty from a diagnostic test / American Journal of Epidiology 121:744~753
Dodd, L. E. / 2003 / Partial AUC estimation and regression / Biometrics 59:614~623
Dong, T. / 2017 / A new diagnostic accuracy measure and cut-point selection criterion / Statistical methods in medical research 26:2832~2852
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UCI(KEPA) : I410-ECN-0101-2020-041-001280536