An Improved Multi-Label Classifier Chain Derived from Binary Relevance

Abstract:

In multi-label classification, each training example is associated with a set of labels and the task for classification is to predict the proper label set for each unseen instance. Recently, multi-label classification methods mainly focus on exploiting the label correlations to improve the accuracy of individual multi-label learner. In this paper, an improved method derived from binary relevance named double layer classifier chaining (DCC) is proposed. This algorithm decomposes the multi-label classification problem into two stages classification process to generate classifier chain. Each classifier in the chain is responsible for learning and predicting the binary association of the label given the attribute space, augmented by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with other well-established methods.

• Periodical: Applied Mechanics and Materials (Volume 551)
• Pages: 302-308
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.551.302
• Online since: May 2014
• Authors: Tao Guo*, Gui Yang Li
• Keywords: Binary Relevance, Classifier Chain, Evaluation Method, Multi-Label Classification

References

[1] G. Tsoumakas, I. Katakis, and I. Vlahavas, Mining Multi-Label Data, in Data Mining and Knowledge Discovery Handbook, 2nd ed., Springer, p.667–685, (2010).

DOI: 10.1007/978-0-387-09823-4_34

[2] Dimou, A., Tsoumakas, G., Mezaris, V., Kompatsiaris, I., Vlahavas, I.: An empirical study of multi-label learning methods for video annotation. In: Proceedings of the 7th International Workshop on Content-Based Multimedia Indexing. IEEE (2009).

DOI: 10.1109/cbmi.2009.37

[3] J. Read, B. Pfahringer, G. Holmes, and E. Frank, Classifier Chains for Multi-Label Classification, Machine Learning, vol. 85, no. 3, p.333–359, (2011).

DOI: 10.1007/s10994-011-5256-5

[4] Shantanu Godbole and Sunita Sarawagi. Discriminative methods for multi-labeled classification. In PAKDD '04: 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 22–30. Springer, (2004).

DOI: 10.1007/978-3-540-24775-3_5

[5] G. Tsoumakas, E. Spyromitros, J. Vilcek, and I. Vlahavas, Mulan: A Java Library for Multi-Label Learning, Journal of Machine Learning Research, vol. 12, p.2411–2414, (2011).

[6] Konstantinos Trohidis, Grigorios Tsoumakas, George Kalliris, Ioannis P. Vlahavas: Multi-label classification of music by emotion. EURASIP J. Audio, Speech and Music Processing 2011: 4 (2011).

DOI: 10.1186/1687-4722-2011-426793

[7] Eduardo Corrêa Gonçalves, Alexandre Plastino, A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains, IEEE Congress on Evolutionary Computation. pp.454-461, (2013).

DOI: 10.1109/ictai.2013.76

[8] Demsar, J. Statistical comparisons of classifiers over multiple data sets. JMRL, 7: 1-30, (2006).