Abstract
This research proposes a novel transfer learning algorithm, Noise- Label Transfer Learning (NLTL), aiming at exploiting noisy (in terms of labels and features) training data to improve the learning quality. We exploit the information from both accurate and noisy data by transferring the features into common domain and adjust the weights of instances for learning. We experiment on three University of California Irvine (UCI) datasets and one real-world dataset (Plurk) to evaluate the effectiveness of the model.
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References
Sáez, J.A., Galar, M., Luengo, J., Herrera, F.: Tackling the Problem of Classification with Noisy Data Using Multiple Classifier Systems: Analysis of the Performance and Robustness. In: Information Science (2013)
Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22, 1345–1359 (2010)
Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for Transfer Learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200 (2007)
Blitzer, J., McDonald, R., Pereira, F.: Domain Adaptation with Structural Correspondence Learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 120–128 (2006)
Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2013)
Meng, X., Wei, F., Liu, X., Zhou, M., Li, S., Wang, H.: Entity-Centric Topic-Oriented Opinion Summarization in Twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 379–387 (2012)
Kuo, T.-T., Hung, S.-C., Lin, W.-S., Peng, N., Lin, S.-D., Lin, W.-F.: Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Network. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 344–348 (2012)
Kuo, T.-T., Hung, S.-C., Lin, W.-S., Lin, S.-D., Peng, T.-C., Shih, C.-C.: Assessing the Quality of Diffusion Models Using Real-World Social Network Data. Technologies and Applications of Artificial Intelligence (TAAI) 2011, 200–205 (2011)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1), 273–324 (1997)
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Lin, WS., Kuo, TT., Huang, YY., Lu, WC., Lin, SD. (2014). A Transfer-Learning Approach to Exploit Noisy Information for Classification and Its Application on Sentiment Detection. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_25
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DOI: https://doi.org/10.1007/978-3-319-13987-6_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
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