Using Co-training and Self-training in Semi-supervised Multiple Classifier Systems
Multiple classifier systems have been originally proposed for supervised classification tasks, and few works have dealt with semi-supervised multiple classifiers. However, there are important pattern recognition applications, such as multi-sensor remote sensing and multi-modal biometrics, which demand semi-supervised multiple classifier systems able to exploit both labelled and unlabelled data. In this paper, the use, in multiple classifier systems, of two well known semi-supervised learning methods, namely, co-training and self-training, is investigated by experiments. Reported results on benchmarking data sets show that co-training and self-training allow exploiting unlabelled data in different types of multiple classifiers systems.
KeywordsFeature Subset Multiple Classifier Classifier Ensemble Multiple Classifier System Supervise Classification Task
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- 2.D’Alchè-Buc, F., Grandvalet, Y., Ambroise, C.: Semi-supervised marginboost. In: Neural Information Processing Systems Foundation, NIPS 2002 (2002)Google Scholar
- 3.Bennet, K., Demiriz, A., Maclin, R.: Exploiting unlabeled data in ensemble methods. In: Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 289–296 (2002)Google Scholar
- 4.El Gayar, N.: An Experimental Study of a Self-Supervised Classifier Ensemble. International Journal of Information Technology 1(1) (2004)Google Scholar
- 5.Zhou, Y., Goldman, S.: Democratic Co-Learning. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), pp. 594–602 (2004)Google Scholar
- 6.Zhu, X.: Semi-supervised learning literature survey, Technical report, Computer Sciences TR 1530, Univ. Wisconsis, Madison, USA (January 2006)Google Scholar
- 7.Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
- 11.Solyman, M., El Gayar, N.F.: A Co-training Approach for Semi-supervised Multiple Classifiers. In: INFO 2006, 4th Int. Conference, Cairo, Egypt, March 25-27 (in press, 2006)Google Scholar