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Multiobjective data mining from solutions by evolutionary multiobjective optimization

Published: 01 July 2017 Publication History

Abstract

One research direction in the field of evolutionary multiobjective optimization (EMO) is a post-analytical process of non-dominated solutions in order to analyze the relationship between design variables and objective functions for optimization problems. For this purpose, data mining techniques have been used in some studies. From a practical point of view, this process itself should be considered as a multiobjective optimization problem. In this paper, multiobjective genetic fuzzy rule selection is applied to the post-analytical process of solutions obtained by EMO algorithms. First, multiple regions of interest are specified in the objective space. Each region with a number of solutions is handled as a different class. A set of patterns is generated by the labeled solutions. Second, a number of fuzzy if-then rules are generated by classification rule mining. Finally, an EMO algorithm is applied to combinatorial optimization of fuzzy if-then rules in order to obtain a number of non-dominated fuzzy classifiers with respect to accuracy and complexity. Through computational experiments using two engineering problems, we show that we can obtain various classifiers with a variety of complexity-accuracy tradeoff.

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Cited By

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  • (2021)Sensitivity Analysis on Constraints of Combinatorial Optimization ProblemsLearning and Intelligent Optimization10.1007/978-3-030-92121-7_30(394-408)Online publication date: 9-Dec-2021
  • (2018)Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00135(745-750)Online publication date: Oct-2018

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    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
    July 2017
    1427 pages
    ISBN:9781450349208
    DOI:10.1145/3071178
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    Published: 01 July 2017

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    Author Tags

    1. data mining
    2. evolutionary multiobjective optimization
    3. genetic fuzzy rule selection
    4. pattern classification

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    GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
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    View all
    • (2021)Sensitivity Analysis on Constraints of Combinatorial Optimization ProblemsLearning and Intelligent Optimization10.1007/978-3-030-92121-7_30(394-408)Online publication date: 9-Dec-2021
    • (2018)Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00135(745-750)Online publication date: Oct-2018

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