In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier. Since the general case is too difficult, in the present work we focus on faulty data from an underfitted model. First, we project the faulty data into a hand-crafted, and thus human readable, intermediate representation (meta-representation, profile vectors), with the aim of separating the two main causes of miss-classification: the classifier is not strong enough, or the data point belongs to an area of the input space where classes are not separable. Second, in the space of these profile vectors, we present a method to fit a meta-classifier (decision tree) and express its output as a set of interpretable (human readable) explanation rules, which leads to several target diagnosis labels: data point is either correctly classified, or faulty due to a too weak model, or faulty due to mixed (overlapped) classes in the input space. Experimental results on several real datasets show more than 80% diagnosis label accuracy and confirm that the proposed intermediate representation allows to achieve a high degree of invariance with respect to the classifier used in the input space and to the dataset being classified, i.e. we can learn the metaclassifier on a dataset with a given classifier and successfully predict diagnosis labels for a different dataset or classifier (or both).
翻译:在本文中,我们提出一种粗略的方法,对某一分类者错误预测的数据集(数据点)中的元素进行个别解释。 由于一般案例太困难, 在目前的工作中,我们侧重于一个设计不足的模型的错误数据。 首先,我们将错误数据投放到手工制作的、因而也是人本可读的中间表述(元表示、剖析矢量)中,目的是区分错误分类的两个主要原因:分类器不够强,或者数据点属于输入空间的一个输入空间的区域,而该输入空间的分类不易分解。第二,在剖析矢量的空域中,我们提出了一个适合元分类器(决定树)的错误数据的方法,并将其输出作为一套可解释(人类可读)的解释规则来表示,这导致几个目标诊断标签:数据点不是被正确分类,就是由于模型太弱,就是由于输入空间的混杂(重叠)类别造成的错误。 数个真实数据设置的实验结果显示超过80%的诊断性标签,或者在这些剖析质矢量的矢量中,我们使用的分类结构能够成功地了解高等级数据。