Towards Explainable Multi-Label Classification

Abstract

Multi-label classification is a very active research area and many real-world applications need efficient multi-label learning. During recent years, explaining machine learning predictions is also a very hot topic. A lot of approaches have been proposed for explaining multi-class classifier predictions. However, almost nothing has been proposed for multi-label and ensemble approaches. This paper brings two main contributions. It first proposes a natural framework consisting in reasoning with base classifier explanations in order to provide explanations for the multi-label predictions. The second contribution focuses on binary relevance, a widely used approach in multi-label classification, and distinguishes two kinds of explanations: common explanations shared by all base classifiers predicting positive labels and joint explanations combining explanations from each base classifier predicting a positive label. The paper proposes an efficient approach for deriving such explanations. Experimental studies show positive results that can be achieved on many multi-label datasets.