ABSTRACT
This paper describes the development of a Co-training (semi-supervised approach) method that uses multiple learners for single target regression on data streams. The experimental evaluation was focused on the comparison between a realistic supervised scenario (all unlabelled examples are discarded) and scenarios where unlabelled examples are used to improve the regression model. Results present fair evidences of error measure reduction by using the proposed Co-training method. However, the error reduction still is relatively small.
- Adebiyi A. Ariyo, Adewumi O. Adewumi, and Charles K. Ayo. Stock price prediction using the arima model. In Proceedings of the 16th International Conference on Computer Modelling and Simulation, UKSIM '14, pages 106--112, Washington, DC, USA, 2014. IEEE Computer Society. Google ScholarDigital Library
- Zhi hua Zhou, Senior Member, and Ming Li. Semi-supervised regression with co-training style algorithms. IEEE Transactions on Knowledge and Data Engineering, page 2007. Google ScholarDigital Library
- Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Conference on Computational Learning Theory, COLT' 98, pages 92--100, New York, NY, USA, 1998. ACM. Google ScholarDigital Library
- Pilsung Kang, Dongil Kim, and Sungzoon Cho. 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. Google ScholarDigital Library
- Albert Bifet and Richard Kirkby. Data stream mining: a practical approach, August 2009.Google Scholar
- Nikunj C. Oza and Stuart Russell. Online bagging and boosting. In In Artificial Intelligence and Statistics 2001, pages 105--112. Morgan Kaufmann, 2001.Google Scholar
- Mohamed Farouk Abdel Hady, Friedhelm Schwenker, and Günther Palm. Semi-supervised learning for regression with co-training by committee. In Proceedings of the 19th ICANN 2009: Part I, ICANN '09, pages 121--130, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarDigital Library
- João Duarte and João Gama. Multi-Target Regression from High-Speed Data Streams with Adaptive Model Rules. In IEEE conference on Data Science and Advanced Analytics, 2015.Google ScholarCross Ref
- João Gama, Raquel Sebastião, and Pedro Pereira Rodrigues. On evaluating stream learning algorithms. Machine Learning, 90(3):317--346, 2013. Google ScholarDigital Library
- K. Bache and M. Lichman. UCI machine learning repository, 2013.Google Scholar
- Albert Bifet, Geoff Holmes, Richard Kirkby, and Bernhard Pfahringer. Moa: Massive online analysis. J. Mach. Learn. Res., 11:1601--1604, August 2010. Google ScholarDigital Library
Index Terms
- Co-training study for online regression
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