C4.5: programs for machine learning
C4.5: programs for machine learning
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Data mining: concepts and techniques
Data mining: concepts and techniques
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Machine Learning in Medical Applications
Machine Learning and Its Applications, Advanced Lectures
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Hybrid learning using genetic algorithms and decision trees for pattern classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Knowledge discovery with classification rules in a cardiovascular dataset
Computer Methods and Programs in Biomedicine
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
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Machine learning algorithms nowadays are important and well-accepted tools which help in demanding and ever-more challenging data analysis in many fields. In this paper, we study an approach to machine learning and knowledge discovery, where a learning algorithm uses experts' domain knowledge to induce solutions, and experts use the algorithm and its solutions to enhance their "information processing strength". An adaptation of evolutionary method AREX for automatic extraction of rules is presented that is based on the evolutionary induction of decision trees and automatic programming. The method is evaluated in a case study on a medical dataset. The obtained results are assessed to evaluate the strength and potential of the proposed classification rules extraction algorithm.