Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
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DOI：
10.1137/1.9781611974010.24
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Hauskrecht M
中科院分区：
文献类型：
--
作者：
Naeini MP;Cooper GF;Hauskrecht M
学习经过良好校准的概率预测模型对于数据挖掘中的许多预测和决策任务至关重要。本文提出了两种新的非参数二元分类模型输出校正方法：基于贝叶斯最优选择的方法和基于贝叶斯模型平均的方法。这些方法的优点是它们独立于用于学习预测模型的算法，并且它们可以在模型学习之后应用于后处理步骤。这使得它们适用于各种机器学习模型和方法。这些校准方法，以及其他方法，在各种数据集的歧视和校准性能方面进行了测试。结果表明，这些方法的性能优于或与最先进的校准方法相当。
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.