An Empirical Study of Lazy Multilabel Classification Algorithms

  • E. Spyromitros
  • G. Tsoumakas
  • Ioannis Vlahavas
Conference paper

DOI: 10.1007/978-3-540-87881-0_40

Volume 5138 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Spyromitros E., Tsoumakas G., Vlahavas I. (2008) An Empirical Study of Lazy Multilabel Classification Algorithms. In: Darzentas J., Vouros G.A., Vosinakis S., Arnellos A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science, vol 5138. Springer, Berlin, Heidelberg

Abstract

Multilabel classification is a rapidly developing field of machine learning. Despite its short life, various methods for solving the task of multilabel classification have been proposed. In this paper we focus on a subset of these methods that adopt a lazy learning approach and are based on the traditional k-nearest neighbor (kNN) algorithm. Two are our main contributions. Firstly, we implement BRkNN, an adaptation of the kNN algorithm for multilabel classification that is conceptually equivalent to using the popular Binary Relevance problem transformation method in conjunction with the kNN algorithm, but much faster. We also identify two useful extensions of BRkNN that improve its overall predictive performance. Secondly, we compare this method against two other lazy multilabel classification methods, in order to determine the overall best performer. Experiments on different real-world multilabel datasets, using a variety of evaluation metrics, expose the advantages and limitations of each method with respect to specific dataset characteristics.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • E. Spyromitros
    • 1
  • G. Tsoumakas
    • 1
  • Ioannis Vlahavas
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece