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Binormal Precision–Recall Curves for Optimal Classification of Imbalanced Data

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  • Zhongkai Liu

    (North Carolina State University)

  • Howard D. Bondell

    (University of Melbourne)

Abstract

Binary classification on imbalanced data, i.e., a large skew in the class distribution, is a challenging problem. Evaluation of classifiers via the receiver operating characteristic (ROC) curve is common in binary classification. Techniques to develop classifiers that optimize the area under the ROC curve have been proposed. However, for imbalanced data, the ROC curve tends to give an overly optimistic view. Realizing its disadvantages of dealing with imbalanced data, we propose an approach based on the Precision–Recall (PR) curve under the binormal assumption. We propose to choose the classifier that maximizes the area under the binormal PR curve. The asymptotic distribution of the resulting estimator is shown. Simulations, as well as real data results, indicate that the binormal Precision–Recall method outperforms approaches based on the area under the ROC curve.

Suggested Citation

  • Zhongkai Liu & Howard D. Bondell, 2019. "Binormal Precision–Recall Curves for Optimal Classification of Imbalanced Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 141-161, April.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:1:d:10.1007_s12561-019-09231-9
    DOI: 10.1007/s12561-019-09231-9
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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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    Cited by:

    1. 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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