Binormal Precision–Recall Curves for Optimal Classification of Imbalanced Data
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DOI: 10.1007/s12561-019-09231-9
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- Kelly Zou & W. J. Hall, 2000. "Two transformation models for estimating an ROC curve derived from continuous data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 621-631.
- Margaret Sullivan Pepe & Tianxi Cai & Gary Longton, 2006. "Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve," Biometrics, The International Biometric Society, vol. 62(1), pages 221-229, March.
- Donald Dorfman & Edward Alf, 1968. "Maximum likelihood estimation of parameters of signal detection theory—A direct solution," Psychometrika, Springer;The Psychometric Society, vol. 33(1), pages 117-124, March.
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- Yifan Zhong & Chuang Cai & Tao Chen & Hao Gui & Jiajun Deng & Minglei Yang & Bentong Yu & Yongxiang Song & Tingting Wang & Xiwen Sun & Jingyun Shi & Yangchun Chen & Dong Xie & Chang Chen & Yunlang She, 2023. "PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
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Keywords
Binary classification; Binormal assumption; Imbalanced data; Precision–Recall curve; ROC curve;All these keywords.
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