Is unlabeled data suitable for multiclass SVM-based web page classification?

ABSTRACT

Support Vector Machines present an interesting and effective approach to solve automated classification tasks. Although it only handles binary and supervised problems by nature, it has been transformed into multiclass and semi-supervised approaches in several works. A previous study on supervised and semi-supervised SVM classification over binary taxonomies showed how the latter clearly outperforms the former, proving the suitability of unlabeled data for the learning phase in this kind of tasks. However, the suitability of unlabeled data for multiclass tasks using SVM has never been tested before. In this work, we present a study on whether unlabeled data could improve results for multiclass web page classification tasks using Support Vector Machines. As a conclusion, we encourage to rely only on labeled data, both for improving (or at least equaling) performance and for reducing the computational cost.

References

1. B. E. Boser, I. Guyon and V. Vapnik. 1992. A Training Algorithm for Optimal Margin Classifiers. Proceedings of the 5th Annual Workshop on computational Learning Theory.
2. C. Campbell. 2000. Algorithmic Approaches to Training Support Vector Machines: A Survey Proceedings of ESANN'2000, European Symposium on Artificial Neural Networks.
3. O. Chapelle, M. Chi y A. Zien 2006. A Continuation Method for Semi-supervised SVMs. Proceedings of ICML'06, the 23rd International Conference on Machine Learning.
4. O. Chapelle, V. Sindhwani, S. Keerthi 2008. Optimization Techniques for Semi-Supervised Support Vector Machines. J. Mach. Learn. Res..
5. C. Cortes and V. Vapnik. 1995. Support Vector Network. Machine Learning.
6. C.-H. Hsu and C.-J. Lin. 2002. A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks.
7. T. Joachims. 1998. Text Categorization with Support Vector Machines: Learning with many Relevant Features. Proceedings of ECML98, 10th European Conference on Machine Learning.
8. T. Joachims. 1999. Transductive Inference for Text Classification Using Support Vector Machines. Proceedings of ICML99, 16th International Conference on Machine Learning.
9. J. Kivinen and E. J. Smola and R. C. Williamson. 2002. Learning with Kernels.
10. T. Mitchell. 1997. Machine Learning. McGraw Hill.
11. H.-N. Qi, J.-G. Yang, Y.-W. Zhong y C. Deng 2004. Multi-class SVM Based Remote Sensing Image Classification and its Semi-supervised Improvement Scheme. Proceedings of the 3rd ICMLC.
12. X. Qi and B. D. Davison. 2007. Web Page Classification: Features and Algorithms. Technical Report LU-CSE-07-010.
13. B. Schölkopf and A. Smola. 1999. Advances in Kernel Methods: Support Vector Learning. MIT Press.
14. F. Sebastiani. 2002. Machine Learning in Automated Text Categorization. ACM Computing Surveys, pp. 1--47.
15. M. P. Sinka and D. W. Corne. 2002. A New Benchmark Dataset for Web Document Clustering. Soft Computing Systems.
16. C. M. Tan, Y. F. Wang and C. D. Lee. 2002. The Use of Bigrams to Enhance Text Categorization. Information Processing and Management.
17. J. Weston and C. Watkins. 1999. Multi-class Support Vector Machines. Proceedings of ESAAN, the European Symposium on Artificial Neural Networks.
18. L. Xu y D. Schuurmans. 2005. Unsupervised and Semi-supervised Multiclass Support Vector Machines. Proceedings of AAAI'05, the 20th National Conference on Artificial Intelligence.
19. Z. Xu, R. Jin, J. Zhu, I. King and M. R. Lyu. 2007. Efficient Convex Optimization for Transductive Support Vector Machine. Advances in Neural Information Processing Systems.
20. Y. Yajima and T.-F. Kuo. 2006. Optimization Approaches for Semi-Supervised Multiclass Classification. Proceedings of ICDM '06 Workshops, the 6th International Conference on Data Mining.

Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning approaches

      • Classification and regression trees

  • Modeling and simulation

    • Model development and analysis

      • Model verification and validation

• Information systems

  • Information retrieval

  • Information storage systems