The Use of Background Knowledge in Decision Tree Induction
Machine Learning
Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
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
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Journal of Artificial Intelligence Research
Information Sciences: an International Journal
| Hi-index | 0.00 |
In the paper, a new method for cost-sensitive learning of decision trees is proposed. Our approach consists in extending the existing evolutionary algorithm (EA) for global induction of decision trees. In contrast to the classical top-down methods, our system searches for the whole tree at the moment. We propose a new fitness function which allows the algorithm to minimize expected cost of classification defined as a sum of misclassification cost and cost of the tests. The remaining components of EA i.e. the representation of solutions and the specialized genetic search operators are not changed. The proposed method is experimentally validated and preliminary results show that the global approach is able to effectively induce cost-sensitive decision trees.