Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine Learning
A Comparison of Genetic Programming Variants for Data Classification
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Evolving accurate and compact classification rules with gene expression programming
IEEE Transactions on Evolutionary Computation
Artificial Intelligence in Medicine
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Extracting accurate and understandable classification rules from data is a fundamental data mining activity. Fuzzy classification rules is considered a better classification of knowledge that the fuzzy rules of readability and analytical, and the use of fuzzy rules is very intuitive. In this paper, we present an improved gene expression programming (GEP) for extracting fuzzy classification rules by a logical operators instead of mathematical ones to represent the chromosome validity evaluation. Moreover, a novel technique to evaluate the fitness of the individual rather than transform the chromosome into expression tree is proposed. Our proposed approach has been tested on some benchmark datasets selected from UCI and the results indicate that our approach is highly comparable with other techniques including basic GEP, C4.5 and C4.5 rule.