Abstract
A fuzzy rule-based classification system (FRBCS) is one of the most popular approaches used in pattern classification problems. One advantage of a fuzzy rule-based system is its interpretability. However, we’re faced with some challenges when generating the rule-base. In high dimensional problems, we can not generate every possible rule with respect to all antecedent combinations. In this paper, by making the use of some data mining concepts, we propose a method for rule generation, which can result in a rule-base containing rules of different lengths. As the next phase, we use rule-weight as a simple mechanism to tune the classifier and propose a new method of rule-weight specification for this purpose. Through computer simulations on some data sets from UCI repository, we show that the proposed scheme achieves better prediction accuracy compared with other fuzzy and non-fuzzy rule-based classification systems proposed in the past.
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References
Arima, M., Hara, E.H., Katzberg, J.D.: A fuzzy logic and rough sets controller for HVAC systems. In: The IEEE WESCANEX, pp. 133–138. New York (1995)
Cordón, O., Herrera, F., Peregrín, A.: Applicability of the fuzzy operators in the design of fuzzy logic controllers. Fuzzy Sets and Systems 86(1), 15–41 (1997)
Glorennec, P.Y.: Application of fuzzy control for building energy management. Building Simulation. International Building Performance Simulation Association, France 1, 197–201 (1991)
Bárdossy, A., Duckstein, L.: Fuzzy rule-based modeling with applications to geophysical. biological and engineering systems. CRC Press, Boca Raton (1995)
Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition. In: Methods that Search for Structures in Data, IEEE Press, Boca Raton (1992)
Chi, Z., Yan, H., Pham, T.: Fuzzy algorithms with applications to image processing and pattern recognition. World Scientific, New York (1996)
Jin, Y.: Fuzzy Modeling of High-dimensional Systems. Complexity Reduction and Interpretability Improvement. IEEE Trans. on Fuzzy Systems, 212–221 (2000)
Jin, Y., Von Seelen, W., Sendhoff, B.: On Generating FC3 Fuzzy Rule Systems from Data Using Evolution Strategies. IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybernetics 29, 829–845 (1999)
Roubos, H., Setnes, M.: Compact and Transparent Fuzzy Models and Classifiers Through Iterative Complexity Reduction. IEEE Trans. on Fuzzy Systems, 516–524 (2001)
Setnes, M., Babuska, R., Verbruggen, B.: Rule-based Modeling: Precision and Transparency. IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews 28, 165–169 (1998)
Nauck, D., Kruse, R.: How the learning of rule weights affects the interpretability of fuzzy systems. In: 7th IEEE International Conference on Fuzzy Systems, pp. 1235–1240. Anchorage (1998)
Ishibuchi, H., Yamamoto, T.: Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. on Fuzzy Systems 13(4), 428–435 (2005)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems 52, 21–32 (1992)
Ishibuchi, H., Nakashima, T., Morisawa, T.: Voting in Fuzzy Rule-Based Systems for Pattern Classification problems. Fuzzy Sets and Systems 103, 223–238 (1999)
Ishibuchi, H., Yamamoto, T.: Comparison of heuristic criteria for fuzzy rule selection in classification problems. Fuzzy Optimization and Decision Making 3(2), 119–139 (2004)
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Fakhrahmad, S.M., Zare, A., Jahromi, M.Z. (2007). Constructing Accurate Fuzzy Rule-Based Classification Systems Using Apriori Principles and Rule-Weighting. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_56
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DOI: https://doi.org/10.1007/978-3-540-77226-2_56
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