An Empirical Study of Lazy Multilabel Classification Algorithms
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.
Unable to display preview. Download preview PDF.
- 1.Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. International Journal of Data Warehousing and Mining 3, 1–13 (2007)Google Scholar
- 4.Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems 14 (2002)Google Scholar
- 6.Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multilabel classification of music into emotions. In: Proc. 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA, USA (2008)Google Scholar
- 7.Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing, pp. 718–721 (2005)Google Scholar