Abstract
In a single-target regression context, some important systems based on data streaming produce huge quantities of unlabeled data (without output value), of which label assignment may be impossible, time consuming or expensive. Semi-supervised methods, that include the co-training approach, were proposed to use the input information of the unlabeled examples in the improvement of models and predictions. In the literature, the co-training methods are essentially applied to classification and operate in batch mode.
Due to these facts, this work proposes a co-training online algorithm for single-target regression to perform model improvement with unlabeled data. This work is also the first-step for the development of online multi-target regressor that create models for multiple outputs simultaneously. The experimental framework compared the performance of this method, when it rejects unalabeled data and when it uses unlabeled data with different parametrization in the training.
The results suggest that the co-training method regressor predicts better when a portion of unlabeled examples is used. However, the prediction improvements are relatively small.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Li, C., Dong, W., Liu, Q., Zhang, X.: MORES: online incremental multiple-output regression for data streams. CoRR, abs/1412.5732 (2014)
Duarte, J., Gama, J.: Multi-target regression from high-speed data streams with adaptive model rules. In: IEEE Conference on Data Science and Advanced Analytics (2015)
Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the ARIMA model. In: Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSIM 2014, Washington, DC, USA, pp. 106–112. IEEE Computer Society (2014)
Zhou, Z.H., Li, M.: Semi-supervised regression with co-training style algorithms. IEEE Trans. Knowl. Data Eng. 19(11), 1479–1493 (2007)
Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models. In: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION 2005) - Volume 1 - Volume 01, WACV-MOTION 2005, Washington, DC, USA, pp. 29–36. IEEE Computer Society (2005)
Chalabi, Z., Mangtani, P., Hashizume, M., Imai, C., Armstrong, B.: Article: time series regression model for infectious disease and weather. Int. J. Environ. Res. 142, 319–327 (2015)
Uslana, V., Seker, H.: Article: quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression. Appl. Soft Comput. 43, 210–221 (2016)
Kang, P., Kim, D., Cho, S.: Semi-supervised support vector regression based on self-training with label uncertainty: an application to virtual metrology in semiconductor manufacturing. Expert Syst. Appl. 51, 85–106 (2016)
Xu, C., Tao, D., Xu., C.: A survey on multi-view learning. CoRR, abs/1304.5634 (2013)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, pp. 92–100. ACM, New York (1998)
Abney, S.P.: Bootstrapping. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 6–12 July 2002, Philadelphia, PA, USA, pp. 360–367 (2002)
Goldman, S., Zhou, Y.: Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th International Conference on Machine Learning, pp. 327–334 (2000)
Abdel Hady, M.F., Schwenker, F., Palm, G.: Semi-supervised learning for regression with co-training by committee. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5768, pp. 121–130. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04274-4_13
Brefeld, U., Gärtner, T., Scheffer, T., Wrobel, S.: Efficient co-regularised least squares regression. In: Proceedings of the 23rd International Conference on Machine learning, ICML 2006, pp. 137–144. ACM, New York (2006)
Gama, J., Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
Acknowledgements
This work is financed under the project “NORTE-01-0145-FEDER-000020” funded by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sousa, R., Gama, J. (2017). Co-training Semi-supervised Learning for Single-Target Regression in Data Streams Using AMRules. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-60438-1_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60437-4
Online ISBN: 978-3-319-60438-1
eBook Packages: Computer ScienceComputer Science (R0)