Hybrid Decision Tree Architecture Utilizing Local SVMs for Multi-Label Classification
- 1.4k Downloads
Multi-label classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the large-scale problem and the high dimensionality of the label space, which have a strong impact on the computational complexity of learning. These problems are especially pronounced for approaches that transform MLC problems into a set of binary classification problems for which SVMs are used. On the other hand, the most efficient approaches to MLC, based on decision trees, have clearly lower predictive performance. We propose a hybrid decision tree architecture that utilizes local SVMs for efficient multi-label classification. We build decision trees for MLC, where the leaves do not give multi-label predictions directly, but rather contain SVM-based classifiers giving multi-label predictions. A binary relevance architecture is employed in each leaf, where a binary SVM classifier is built for each of the labels relevant to that particular leaf. We use several real-world datasets to evaluate the proposed method and its competition. Our hybrid approach on almost every classification problem outperforms the predictive performances of SVM-based approaches while its computational efficiency is significantly improved as a result of the integrated decision tree.
Keywordsmulti-label classification hybrid architecture
Unable to display preview. Download preview PDF.
- 1.Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 3.Dong, G.M., Chen, J.: Study on support vector machine based decision tree and application. In: Proc. of the 5th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 318–322 (2008)Google Scholar
- 4.Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.: Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 7.Katakis, I., Tsoumakas, G., Vlahavas, I.: Multilabel Text Classification for Automated Tag Suggestion. In: Proc. of the ECML/PKDD Discovery Challenge (2008)Google Scholar
- 10.Read, J., Pfahringer, B., Holmes, G.: Multi-label Classification Using Ensembles of Pruned Sets. In: Proc. of the 8th IEEE International Conference on Data Mining, pp. 995–1000 (2008)Google Scholar
- 12.Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proc. of the 14th Annual ACM International Conference on Multimedia, pp. 421–430 (2006)Google Scholar
- 15.Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective and Efficient Multilabel Classification in Domains with Large Number of Labels. In: Proc. of the ECML/PKDD Workshop on Mining Multidimensional Data, pp. 30–44 (2008)Google Scholar
- 16.Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Heidelberg (2010)Google Scholar