C4.5: programs for machine learning
C4.5: programs for machine learning
Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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
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
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and 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
Mixed decision trees: an evolutionary approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Evolutionary learning of linear trees with embedded feature selection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary design of decision trees for medical application
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Information Sciences: an International Journal
| Hi-index | 0.00 |
In the paper, a new method of decision tree learning for cost-sensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed approach optimal trees are searched in a global manner by using an evolutionary algorithm (EA). Specialized genetic operators are applied to modify both the tree structure and tests in non-terminal nodes. A suitably defined fitness function enables the algorithm to minimize the misclassification cost instead of the number of classification errors. The performance of the EA-based method is compared to three well-recognized algorithms on real-life problems with known and randomly generated cost-matrices. Obtained results show that the proposed approach is competitive both in terms of misclassification cost and compactness of the classifier at least for some datasets.