Non-Weighted Evaluation Function in Multi-Objective Problems
Keywords:Fuzzy Rule extraction, Genetic Algorithms, Non Weighted Evaluation Function, Mutation complementary method
An evaluation function is proposed to deal with multi-objective problems without weight using a new composition method. Improving the evaluation function by reducing its complexity through discarding the weights. The evaluation function is utilized for the optimization of fuzzy rules.
A genetic algorithm is applied as a multi-objective algorithm for fuzzy rules extraction. Simplicity during building fuzzy inference system and reducing the computational complexity is required. The algorithm is applied on AFR data sets.
â€¢ Dehuri, S., Patnaik, S., Ghosh, A., & Mall, R. (2008). Application of elitist multi-objective genetic algorithm for classification rule generation. Applied soft computing, 8(1), 477-487.
â€¢ FernÃ¡ndez, A., LÃ³pez, V., del Jesus, M. J., & Herrera, F. (2015). Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems.
â€¢ Gacto, M. J., AlcalÃ¡, R., & Herrera, F. (2010). Integration of an index to preserve the semantic interpretability in the multiobjective evolutionary rule selection and tuning of linguistic fuzzy systems. Fuzzy Systems, IEEE Transactions on, 18(3), 515-531.
â€¢ Gacto, M. J., AlcalÃ¡, R., & Herrera, F. (2011). Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences, 181(20), 4340-4360.
â€¢ Huysmans, J., Baesens, B., & Vanthienen, J. (2006). Using rule extraction to improve the comprehensibility of predictive models. Available at SSRN 961358.
â€¢ Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51(1), 141-154.
â€¢ Ishibuchi, H., Nakashima, T., & Murata, T. (2001). Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences, 136(1), 109-133.
â€¢ Ishibuchi, H., & Nojima, Y. (2007). Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. International Journal of Approximate Reasoning, 44(1), 4-31.
â€¢ Ishibuchi, H., & Yamamoto, T. (2004). 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.
â€¢ Mandal, S., & Pal, M. K. K. S. K. (2011). Pattern Recognition and Machine Intelligence.
â€¢ Noda, E., Freitas, A., & Lopes, H. S. (1999). Discovering interesting prediction rules with a genetic algorithm. Paper presented at the Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on.
â€¢ Noda, E., Freitas, A. A., & Yamakami, A. (2003). A Distributed-Population GA for Discovering Interesting Prediction Rules Advances in Soft Computing (pp. 287-296): Springer.
â€¢ Xu, J., & Zhou, X. (2011). Fuzzy-like multiple objective decision making (Vol. 263): Springer.
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