Background Check: A General Technique to Build More Reliable and Versatile Classifiers
Background Check: A General Technique to Build More Reliable and Versatile Classifiers
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DOI：
10.1109/icdm.2016.0150
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发表时间：
2016-12
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通讯作者：
Miquel Perello-Nieto;Telmo de Menezes e Silva Filho;Meelis Kull;Peter A. Flach
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作者：
Miquel Perello-Nieto;Telmo de Menezes e Silva Filho;Meelis Kull;Peter A. Flach
我们引入了一种强大的技术，使分类器更加可靠和通用。背景检查使分类器能够评估未标记的测试数据与训练数据的差异。特别是，背景检查使分类器能够(i)使用拒绝选项执行谨慎分类，（ii）识别异常值，以及（iii）更好地评估其预测的置信度。我们从第一性原理推导出该方法，并考虑了背景和前景分布之间的四种特殊关系。其中一个假设与两个参数之间存在仿射关系，我们将展示这个二元参数空间如何自然地在上述功能之间插入。我们通过将该方法与已发表的用于41个基准数据集的离群值检测和可信分类的专用解决方案进行实验比较，证明了该方法的多功能性。结果表明，背景检查可以匹配并在许多情况下超过专业方法的性能。
We introduce a powerful technique to make classifiers more reliable and versatile. Background Check equips classifiers with the ability to assess the difference of unlabelled test data from the training data. In particular, Background Check gives classifiers the capability to (i) perform cautious classification with a reject option, (ii) identify outliers, and (iii) better assess the confidence in their predictions. We derive the method from first principles and consider four particular relationships between background and foreground distributions. One of these assumes an affine relationship with two parameters, and we show how this bivariate parameter space naturally interpolates between the above capabilities. We demonstrate the versatility of the approach by comparing it experimentally with published special-purpose solutions for outlier detection and confident classification on 41 benchmark datasets. Results show that Background Check can match and in many cases surpass the performances of specialised approaches.