Abstract
Adding fuzziness to normal classification rules enables the rules to adapt to the real-life decision-making process. Besides, it also adds to the classification accuracy of the obtained model and the rules look more accurate and reasonable. Further improvement in classification accuracy can be achieved by discovering exceptions corresponding to these fuzzy rules. Fuzzy rules augmented with exceptions (censors) are termed as Fuzzy Censored Classification Rules (FCCRs) and such kind of rules are best at handling uncertainties like vagueness and ambiguity. These rules, being very efficient, have been widely used under exceptional circumstances. In this paper, we have investigated all the algorithms used in past for discovering FCCRs. Based on review of literature, we have also proposed possible modifications to existing algorithms and techniques.
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References
Driankov, D., Hellendoorn, H.: Fuzzy logic with unless-rules. In: IEEE International Conference on Fuzzy Systems, pp. 255–262. IEEE (1992)
Ishibuchi, H., Nakshima T.: Fuzzy classification with reject options by fuzzy if-then rules. In: IEEE World Congress on Computational Intelligence, pp. 1452–1457. IEEE, Anchorage, AK (1998)
Van den Bergh, W.M., Van den Berg, J., Kaymak, U.: Detecting noise trading using fuzzy exception learning. In: IFSA World Congress and 20th NAFIPS International Conference, pp. 946–951. IEEE, Vancouver (2001)
Carmona, P., Castro, J.L., Zurita, J.M.: Fuzzy rule identification with exceptions. IEEE Transactions on Fuzzy Systems, 12(1), 140–151 (2004)
Yao, Y., Wang, F.Y., Wang, J.: “Rule + exception” strategies for knowledge management and discovery. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining and Granular-Soft Computing, pp. 69–78. Springer Berlin Heidelberg (2005)
Carmona, P., Castro, J.L.: An Ant Colony Optimization plug-into enhance the interpretability of fuzzy rule bases with exceptions. In: Analysis and Design of Intelligent Systems using Soft Computing Techniques, pp. 436–444. Springer Berlin Heidelberg (2007)
Carmona, P., Castro, J.L.: Improvements in the identification of interpretable fuzzy models with exceptions based on ant colony optimization. In: 4th IEEE International Conference on Intelligent Systems, pp. 2–39. IEEE, Varna (2008)
Siddiqui, T., Alam, M.A.: Discovery of Fuzzy Censored Production Rules from Large Set of Discovered Fuzzy if then Rules. In: Proceedings of World Academy of Science, Engineering and Technology [PWASET], pp. 158–161 (2009)
Bala, R., Ratnoo, S.: Discovering Fuzzy Censored Classification Rules (FCCRS): A Genetic Algorithm Approach. International Journal of Artificial Intelligence & Applications, 3(4), 175–188 (2012)
Bala, R., Ratnoo, S.: A Genetic Algorithm Approach for Discovering Tuned Fuzzy Classification Rules with Intra-and Inter-Class Exceptions. Journal of Intelligent Systems, 25(2), 263–282 (2016)
Vashishtha, J., Kumar, D., Ratnoo, S.: An evolutionary approach to discover intra–and inter–class exceptions in databases. International Journal of Intelligent Systems Technologies and Applications, 12(3–4), 283–300 (2013)
Pathak, A., Vashistha, J.: Classification Rule and Exception Mining Using Nature Inspired Algorithms. International Journal of Computer Science and Information Technologies, 6(3), 3023–3030 (2015)
Suzuki, E.: Undirected discovery of interesting exception rules. International Journal of Pattern Recognition and Artificial Intelligence, 16(08), 1065–1086 (2002)
Suzuki, E., Żytkow, J.M.: Unified algorithm for undirected discovery of exception rules. International journal of intelligent systems, 20(7), 673–691 (2005)
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Pathak, A., Goel, D., Debnath, S. (2018). Mining Fuzzy Classification Rules with Exceptions: A Comparative Study. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_13
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DOI: https://doi.org/10.1007/978-981-10-6890-4_13
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