Towards identifying causal relation between instances and labels

Abstract

Multi-Instance Multi-Label (MIML) learning is a popular framework in machine learning, where each object is represented by a bag of instances, and associated with multiple labels. While MIML learning has achieved success in many applications, it is less clear how the labels are related to the instances. In this paper, we propose to study the causal relation between instances and labels, which on one hand can improve the interpretability of complicated MIML models, and on the other hand may further improve the prediction performance at both instance and bag levels. We exploit prototypes in the instance space as a bridge to represent the examples, and then propose an efficient algorithm to identify the causal relations from prototypes to class labels, which are further utilized for model training and key instance detection. Experiments on various datasets show that in addition to superior classification performance, our approach can identify reasonable causal relations between instances and labels.