Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
SVM based adaptive learning method for text classification from positive and unlabeled documents
Knowledge and Information Systems
Bayesian network based business information retrieval model
Knowledge and Information Systems
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
A genetic algorithm based heuristic to the multi-period fixed charge distribution problem
Applied Soft Computing
Adding chaos to differential evolution for range image registration
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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The process of automatically extracting novel, useful and ultimately comprehensible information from large databases, known as data mining, has become of great importance due to the ever-increasing amounts of data collected by large organizations. In particular, the emphasis is devoted to heuristic search methods able to discover patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. In this paper an evolutionary system capable of extracting explicit classification rules is presented. Special interest is dedicated to find easily interpretable rules that may be used to make crucial decisions. A comparison with the findings achieved by other methods on a real problem, the breast cancer diagnosis, is performed.