Multiobjective data mining from solutions by evolutionary multiobjective optimization

Nojima，Yusuke1; Tanigaki，Yuki1; Ishibuchi，Hisao2

10.1145/3071178.3080293

2017-07-01

GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

617-624

Berlin, Germany

1515 BROADWAY, NEW YORK, NY 10036-9998 USA

Association for Computing Machinery, Inc

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.

Data mining Evolutionary multiobjective optimization Genetic fuzzy rule selection Pattern classification.

其他

英语

EI ; CPCI-S

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:000530095200078

20173104005679

Combinatorial optimization ; Data mining ; Evolutionary algorithms ; Fuzzy inference ; Fuzzy rules ; Fuzzy sets ; Pattern recognition ; Solution mining

Mine and Quarry Operations:502.1 ; Data Processing and Image Processing:723.2 ; Expert Systems:723.4.1 ; Optimization Techniques:921.5

2-s2.0-85026366331

Scopus

1.Osaka Prefecture University,Sakai, Osaka,Japan
2.Southern University of Science and Technology (SUSTech),Shenzhen, Guangdong,China

Nojima，Yusuke,Tanigaki，Yuki,Ishibuchi，Hisao. Multiobjective data mining from solutions by evolutionary multiobjective optimization[C]. 1515 BROADWAY, NEW YORK, NY 10036-9998 USA:Association for Computing Machinery, Inc,2017:617-624.