Abstract
In this chapter, we introduce the concepts of both generative learning and multi-task learning, and presents a proposed fuzzy approach for multi-task classification. We also discuss the advantages of fuzzy classification in the context of generative multi-task learning, in comparison with traditional classification in the context of discriminative single-task learning.
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References
H. Liu, M. Cocea, A. Mohasseb, and M. Bader. 2017. Transformation of discriminative single-task classification into generative multi-task classification in machine learning context. In International Conference on Advanced Computational Intelligence, Doha, Qatar, 4–6 February 2017, 66–73.
Zadeh, L. 2015. Fuzzy logic: A personal perspective. Fuzzy Sets and Systems 281: 4–20.
Ross, T. 2010. Fuzzy Logic with Engineering Applications. West Sussex: Wiley.
Chen, S.-M. 1996. A fuzzy reasoning approach for rule-based systems based on fuzzy logics. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 26 (5): 769–778.
H. Liu and M. Cocea. 2017. Fuzzy rule based systems for interpretable sentiment analysis. In International Conference on Advanced Computational Intelligence, Doha, Qatar, 4–6 February 2017, 129–136.
Berthold, M.R. 2003. Mixed fuzzy rule formation. International Journal of Approximate Reasoning 32: 67–84.
Berthold, M.R., B. Wiswedel, and T.R. Gabriel. 2013. Fuzzy logic in knime: Modules for approximate reasoning. International Journal of Computational Intelligence Systems 6 (1): 34–45.
Wang, L.-X., and J.M. Mendel. 1992. Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man and Cybernetics 22 (6): 1414–1427.
Chen, S.-M., and L.-W. Lee. 2010. Fuzzy decision-making based on likelihood-based comparison relations. IEEE Transactions on Fuzzy Systems 18 (3): 613–628.
H. Liu and M. Cocea. Fuzzy information granulation towards interpretable sentiment analysis. Granular Computing 3 (1), In press.
M. Lichman. 2013. UCI machine learning repository. http://archive.ics.uci.edu/ml
Tan, P.-N., M. Steinbach, and V. Kumar. 2006. Introduction to Data Mining. New Jersey: Pearson Education.
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Liu, H., Cocea, M. (2018). Fuzzy Classification Through Generative Multi-task Learning. In: Granular Computing Based Machine Learning. Studies in Big Data, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-70058-8_5
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DOI: https://doi.org/10.1007/978-3-319-70058-8_5
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