Multilabel classification with meta-level features

Abstract

Effective learning in multi-label classification (MLC) requires an appropriate level of abstraction for representing the relationship between each instance and multiple categories. Current MLC methods have been focused on learning-to-map from instances to ranked lists of categories in a relatively high-dimensional space. The fine-grained features in such a space may not be sufficiently expressive for characterizing discriminative patterns, and worse, make the model complexity unnecessarily high. This paper proposes an alternative approach by transforming conventional representations of instances and categories into a relatively small set of link-based meta-level features, and leveraging successful learning-to-rank retrieval algorithms (e.g., SVM-MAP) over this reduced feature space. Controlled experiments on multiple benchmark datasets show strong empirical evidence for the strength of the proposed approach, as it significantly outperformed several state-of-the-art methods, including Rank-SVM, ML-kNN and IBLR-ML (Instance-based Logistic Regression for Multi-label Classification) in most cases.