C4.5: programs for machine learning
C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Discovery of Decision Rules from Databases: An Evolutionary Approach
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Search-intensive concept induction
Evolutionary Computation
Journal of Artificial Intelligence Research
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Cost-Sensitive Decision Trees with Pre-pruning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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Most of classification learning methods aim at the reduction of the number of errors. However, in many real-life applications it is misclassification cost, which should be minimized. In the paper we propose a new method for cost-sensitive learning of decision rules from datasets. Our approach consists in modifying the existing system EDRL-MD (Evolutionary Decision Rule Learner with Multivariate Discretization). EDRL-MD learns decision rules using an evolutionary algorithm (EA). We propose a new fitness function, which allows the algorithm to minimize misclassification cost rather than the number of classification errors. The remaining components of EA i.e., the representation of solutions and the genetic search operators are not changed. The performance of our method is compared to that of C5.0 learning system. The results show, that the modified EDRL-MD is able to effectively process datasets with non-equal error costs.