Abstract
Semi supervised learning methods have gained importance in today’s world because of large expenses and time involved in labeling the unlabeled data by human experts. The proposed hybrid approach uses SVM and Label Propagation to label the unlabeled data. In the process, at each step SVM is trained to minimize the error and thus improve the prediction quality. Experiments are conducted by using SVM and logistic regression(Logreg). Results prove that SVM performs tremendously better than Logreg. The approach is tested using 12 datasets of different sizes ranging from the order of 1000s to the order of 10000s. Results show that the proposed approach outperforms Label Propagation by a large margin with F-measure of almost twice on average. The parallel version of the proposed approach is also designed and implemented, the analysis shows that the training time decreases significantly when parallel version is used.
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References
Castelli, V., Cover, T.: The exponential value of labeled samples. Pattern Recogn. Lett. 16, 105–111 (1995)
Castelli, V., Cover, T.: The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Trans. Inf. Theory 42, 2101–2117 (1996)
Ratsaby, J., Venkatesh, S.: Learning from a mixture of labeled and unlabeled examples with parametric side information. In: Proceedings of the Eighth Annual Conference on Computational Learning Theory, pp. 412–417 (1995)
Cozman, F.G., Cohen, I., Cirelo, M.C.: Semi-supervised learning of mixture models. In: ICML (2003)
Corduneanu, A., Jaakkola, T.: Stable mixing of complete and incomplete information, Technical report AIM-2001-030, MIT AI Memo (2001)
Callison-Burch, C., Talbot, D., Osborne, M.: Statistical machine translation with word- and sentence-aligned parallel corpora. In: Proceedings of the ACL (2004)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39, 103–134 (2000)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B, 1–38 (1977)
Bennett, K., Demiriz, A.: Semi-supervised support vector machines. Adv. Neural Inf. Proc. Syst. 11, 368–374 (1999)
Dara, R., Kremer, S., Stacey, D.: Clustering unlabeled data with SOMs improves classification of labeled real-world data. In: Proceedings of the World Congress on Computational Intelligence (WCCI) (2002)
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196 (1995)
Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Seventh Conference on Natural Language Learning (CoNLL-2003) (2003)
Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self training of object detection models. In: Seventh IEEE Workshop on Applications of Computer Vision (2005)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Workshop on Computational Learning Theory, COLT (1998)
Balcan, M.-F., Blum, A.: An augmented PAC model for semi-supervised learning. In: Chapelle, O., Sch Olkopf, B., Zien, A. (eds.) Semi-supervised learning. MIT Press, Cambridge (2006)
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical report CMU-CALD-02-107, Carnegie Mellon University (2002)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Trans. Knowl. Data Eng. 20(1), 55–67 (2008)
Zhu, X., Lafferty, J., Rosenfeld, R.: Semi-supervised learning with graphs. Diss. Carnegie Mellon University, Language Technologies Institute, School of Computer Science (2005)
Hearst, M.A., et al.: Support vector machines. IEEE Intel. Syst. Appl. 13(4), 18–28 (1998)
Acknowledgments
We are thankful for the support provided by the Department of Computer Science and Informations Systems, BITS, Pilani, K.K. Birla Goa Campus to carry out the experimental analysis.
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Govada, A., Joshi, P., Mittal, S., Sahay, S.K. (2015). Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_14
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DOI: https://doi.org/10.1007/978-3-319-21024-7_14
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