Title :
Genetic lateral tuning of membership functions as post-processing for hybrid fuzzy genetics-based machine learning
Author :
Nojima, Yusuke ; Takahashi, Yuji ; Ishibuchi, Hisao
Author_Institution :
Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan
Abstract :
Genetic fuzzy systems (GFS) have been actively studied in the field of fuzzy classifier design. GFS can generate simple and accurate classifiers with a number of fuzzy if-then rules by evolutionary computation (EC). One general concern is how to discretize numerical attributes into fuzzy partitions. Most GFS use homogeneous fuzzy partitions without considering the class distribution of each attribute. This is the simplest idea, but more accurate classifiers could be obtained by optimizing fuzzy partitions. There are three approaches. One is pre-processing where inhomogeneous fuzzy partitions are specified according to the class distribution before applying EC. Another approach is to simultaneously optimize both a set of fuzzy if-then rules and fuzzy partitions by EC. The other is post-processing where fuzzy partitions used in the obtained classifier are optimized afterward. In this paper, we examine the effect of post-processing where fuzzy partition optimization is applied to the obtained classifier by GFS. In computational experiments, we first use our hybrid fuzzy genetics-based machine learning with homogeneous fuzzy partitions for standard data sets and its parallel distributed implementation for large data sets. Then we apply genetic lateral tuning as post-processing to shift the positions of membership functions according to the pattern distribution.
Keywords :
fuzzy set theory; genetic algorithms; learning (artificial intelligence); pattern classification; GFS; class distribution; evolutionary computation; fuzzy classifier design; fuzzy if-then rule; fuzzy partition optimization; genetic fuzzy systems; genetic lateral tuning; homogeneous fuzzy partition; hybrid fuzzy genetics-based machine learning; membership function; numerical attribute; parallel distributed implementation; pattern distribution; post-processing; Distributed databases; Genetics; Sociology; Standards; Statistics; Training; Tuning; fuzzy classifier design; genetic lateral tuning; genetics-based machine learnig; pattern classification;
Conference_Titel :
Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on
DOI :
10.1109/SCIS-ISIS.2014.7044847