On Active Learning in Multi-label Classification

Abstract

In conventional multiclass classification learning, we seek to induce a prediction function from the domain of input patterns to a mutually exclusive set of class labels. As a straightforward generalization of this category of learning problems, so-called multi-label classification allows for input patterns to be associated with multiple class labels simultaneously. Text categorization is a domain of particular relevance which can be viewed as an instance of this setting. While the process of labeling input patterns for generating training sets already constitutes a major issue in conventional classification learning, it becomes an even more substantial matter of relevance in the more complex multi-label classification setting. We propose a novel active learning strategy for reducing the labeling effort and conduct an experimental study on the well-known Reuters-21578 text categorization benchmark dataset to demonstrate the efficiency of our approach.