论文标题
解释拒绝学习矢量量化分类器的选项
Explaining Reject Options of Learning Vector Quantization Classifiers
论文作者
论文摘要
尽管通常假定机器学习模型总是输出预测，但也存在拒绝选项形式的扩展，该模型允许模型拒绝输入，而只有一个可获得不可接受的低确定性的预测。随着可解释AI的持续兴起，已经开发了许多解释模型预测的方法。但是，了解为什么拒绝给定输入的原因，而不是由模型进行分类，这也很有趣。令人惊讶的是，到目前为止尚未考虑拒绝的解释。 我们建议使用反事实解释来解释拒绝，并研究如何有效地计算重要类别模型的不同拒绝选项的反事实解释，即基于原型的分类器，例如学习向量量化模型。
While machine learning models are usually assumed to always output a prediction, there also exist extensions in the form of reject options which allow the model to reject inputs where only a prediction with an unacceptably low certainty would be possible. With the ongoing rise of eXplainable AI, a lot of methods for explaining model predictions have been developed. However, understanding why a given input was rejected, instead of being classified by the model, is also of interest. Surprisingly, explanations of rejects have not been considered so far. We propose to use counterfactual explanations for explaining rejects and investigate how to efficiently compute counterfactual explanations of different reject options for an important class of models, namely prototype-based classifiers such as learning vector quantization models.
