Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Learning Control Strategies for Chemical Processes: A Distributed Approach
IEEE Expert: Intelligent Systems and Their Applications
Knowledge discovery from data?
IEEE Intelligent Systems
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Machine learning applied to quality management-A study in ship repair domain
Computers in Industry
Classifier fitness based on accuracy
Evolutionary Computation
Learning cross-level certain and possible rules by rough sets
Expert Systems with Applications: An International Journal
A fashion mix-and-match expert system for fashion retailers using fuzzy screening approach
Expert Systems with Applications: An International Journal
A hybrid model using genetic algorithm and neural network for classifying garment defects
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A genetic algorithm-based rule extraction system
Applied Soft Computing
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Recently, use of a Learning Classifier System (LCS) has become promising method for performing classification tasks and data mining. For the task of classification, the quality of the rule set is usually evaluated as a whole rather than evaluating the quality of a single rule. The present investigation proposes a hybrid of the C4.5 rule induction algorithm and a GA (Genetic Algorithm) approach to extract an accuracy based rule set. At the initial stage, C4.5 is applied to a classification problem to generate a rule set. Then, the GA is used to refine the rules learned. Using eight well-known data sets, it has been shown that the present work, in comparison to C4.5 alone and UCS, provides a marked improvement in a number of cases.