Abstract
Genetic fuzzy rule selection is an effective approach to the design of accurate and interpretable fuzzy rule-based classifiers. It tries to minimize the complexity of fuzzy rule-based classifiers while maximizing their accuracy by selecting only a small number of fuzzy rules from a large number of candidate rules. One important issue in genetic fuzzy rule selection is the prescreening of candidate rules. If we use too many candidate rules, a large computation load is required to search for good rule sets. On the other hand, good rule sets will not be found when promising fuzzy rules are not included in the candidate rule set. It is essential for the success of genetic fuzzy rule selection to use a tractable number of promising fuzzy rules as candidate rules. In this paper, we propose an idea of using Pareto-optimal and near Pareto-optimal fuzzy rules as candidate rules in genetic fuzzy rule selection. Pareto-optimality is defined by two well-known data mining criteria: support and confidence. To extract not only Paretooptimal but also near Pareto-optimal fuzzy rules, we modify Pareto dominance using a dominance margin ε . Through computational experiments, we examine the effect of the proposed idea on multiobjective genetic fuzzy rule selection.
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References
Agrawal, R., et al.: Fast Discovery of Association Rules. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)
Casillas, J., et al. (eds.): Interpretability Issues in Fuzzy Modeling. Springer, Berlin (2003)
Casillas, J., et al. (eds.): Accuracy Improvements in Linguistic Fuzzy Modeling. Springer, Berlin (2003)
Coenen, F., Leng, P.: Obtaining Best Parameter Values for Accurate Classification. In: Proc. of 5th IEEE International Conference on Data Mining, pp. 549–552. IEEE Computer Society Press, Los Alamitos (2005)
de la Iglesia, B., et al.: Data Mining Rules using Multi-Objective Evolutionary Algorithms. In: Proc. of 2003 Congress on Evolutionary Computation, pp. 1552–1559 (2003)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Deb, K., Mohan, M., Mishra, S.: Evaluating the e-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation 14(4), 501–525 (2005)
Deb, K., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Ishibuchi, H., Kuwajima, I., Nojima, Y.: Relation between Pareto-Optimal Fuzzy Rules and Pareto-Optimal Fuzzy Rule Sets. In: Proc. of 1st IEEE Symposium on Computational Intelligence in Multicriteria Decision Making. IEEE Computer Society Press, Los Alamitos (in press, 2007)
Ishibuchi, H., Murata, T., Turksen, I.B.: Single-Objective and Two-Objective Genetic Algorithms for Selecting Linguistic Rules for Pattern Classification Problems. Fuzzy Sets and Systems 89 89(2), 135–150 (1997)
Ishibuchi, H., Nakashima, T., Murata, T.: Three-Objective Genetics-based Machine Learning for Linguistic Rule Extraction. Information Sciences 136(1-4), 109–133 (2001)
Ishibuchi, H., Nakashima, T., Nii, M.: Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining. Springer, Berlin (2004)
Ishibuchi, H., et al.: Selecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algorithms. IEEE Trans. on Fuzzy Systems 3(3), 260–270 (1995)
Ishibuchi, H., Yamamoto, T.: Fuzzy Rule Selection by Multi-Objective Genetic Local Search Algorithms and Rule Evaluation Measures in Data Mining. Fuzzy Sets and Systems 141(1), 59–88 (2004)
Kaya, M.: Multi-Objective Genetic Algorithm based Approaches for Mining Optimized Fuzzy Association Rules. Soft Computing 10, 578–586 (2006)
Laumanns, M., et al.: Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation 10(3), 263–282 (2002)
Luke, S., Panait, L.: Lexicographic Parsimony Pressure. In: Proc. of 2002 Genetic and Evolutionary Computation Conference, pp. 829–836 (2002)
Reynolds, A., de la Iglesia, B.: Rule Induction using Multi-Objective Metaheuristics: Encouraging Rule Diversity. In: Proc. of 2006 International Joint Conference on Neural Networks, pp. 6375–6382 (2006)
Wang, H., et al.: Multi-Objective Hierarchical Genetic Algorithm for Interpretable Fuzzy Rule-based Knowledge Extraction. Fuzzy Sets and Systems 149(1), 149–186 (2005)
Wang, H., et al.: Agent-based Evolutionary Approach for Interpretable Rule-based Knowledge Extraction. IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews 35(2), 143–155 (2005)
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Ishibuchi, H., Kuwajima, I., Nojima, Y. (2007). Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_39
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DOI: https://doi.org/10.1007/978-3-540-72432-2_39
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