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A technical note on the paper 'hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems'
- Source :
- Applied Soft Computing. 41:91-93
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) 1. They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model.
- Subjects :
- Mathematical optimization
021103 operations research
Fuzzy classification
Fuzzy rule
0211 other engineering and technologies
02 engineering and technology
Fuzzy logic
Set (abstract data type)
Genetic algorithm
Genetic fuzzy systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Integer programming
Algorithm
Software
Statistical hypothesis testing
Mathematics
Subjects
Details
- ISSN :
- 15684946
- Volume :
- 41
- Database :
- OpenAIRE
- Journal :
- Applied Soft Computing
- Accession number :
- edsair.doi...........3fd8673eada2f8bd2cf3fc523ded570c