朴素贝叶斯分类器与生成森林的局部鲁棒性量化：一种通用方法
Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach
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- Ghent University（根特大学）
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中文总结 AI 辅助
本文提出一种通用方法，量化朴素贝叶斯分类器和生成森林在概率图模型下的预测鲁棒性，通过多种扰动邻域评估，并以鲁棒性值作为预测可信度的指标。
中文摘要 AI 辅助
我们提供了计算两类生成式分类器预测鲁棒性的方法，这两类分类器的底层分布为概率图模型（PGM）：朴素贝叶斯分类器和生成森林（随机森林的概率扩展）。遵循鲁棒性量化的范式，我们将预测的鲁棒性定义为在不改变该预测的前提下，分类器分布可被扰动的程度。我们考虑通过在一般邻域内变化PGM的局部模型所获得的扰动，并特别关注ε-污染、全变差距离和卡方散度球。我们在基准数据集上测试了我们的方法，证明了预测的鲁棒性值可作为其可信度的指标，并将我们的方法与其他此类指标进行了比较。
英文摘要
We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM): naive Bayes classifiers and generative forests (a probabilistic extension of random forests). Following the paradigm of robustness quantification, we define the robustness of a prediction as the extent to which the distribution of the classifier can be perturbed without changing this prediction. We consider perturbations obtained by varying the local models of the PGMs within general neighborhoods and focus in particular on epsilon-contamination, total variation distance and chi-squared divergence balls. We test our methods on benchmark datasets, demonstrate that the robustness value of a prediction serves as an indicator for its trustworthiness and compare our approach with other such indicators.
