Multilabel classification with meta-level features

Effective learning in multi-label classification (MLC) requires an ate 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...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Siddharth Gopal, Yiming Yang
• Keywords: Current MLC Methods, Information Management, Multi-label Classification, Multiple Categories, SIGIR 2010