C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Mining multiple comprehensible classification rules using genetic programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Solving classification problems using genetic programming algorithms on GPUs
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
An incremental approach to genetic-algorithms-based classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
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In this paper a Grammar Guided Genetic Programmingbased method for the learning of rule-based classification systems is proposed. The method learns disjunctive normal form rules generated by means of a context-free grammar. The individual constitutes a rule based decision list that represents the full classifier. To overcome the problem of computational time of this system, it parallelizes the evaluation phase reducing significantly the computation time. Moreover, different operator genetics are designed to maintain the diversity of the population and get a compact set of rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.