C4.5: programs for machine learning
C4.5: programs for 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
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
Data Mining and Knowledge Discovery
On the quest for easy-to-understand splitting rules
Data & Knowledge Engineering
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
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In some classification problems, apart from a good model, we might be interested in obtaining succinct explanations for particular classes. Our goal is to provide simpler classification models for these classes without a significant accuracy loss. In this paper, we propose some modifications to the splitting criteria and the pruning heuristics used by standard top-down decision tree induction algorithms. This modifications allow us to take each particular class importance into account and lead us to simpler models for the most important classes while, at the same time, the overall classifier accuracy is preserved.