C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
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
Issues in Classifier Evaluation using Optimal Cost Curves
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Decision tree grafting from the all-tests-but-one partition
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
A unifying view on dataset shift in classification
Pattern Recognition
| Hi-index | 0.00 |
This paper investigates the effect of class distribution on the predictive performance of classification models using cost-sensitive learning, rather than the sampling approach employed previously by a similar study. The predictive performance is measured using the cost space representation, which is a dual to the ROC representation. This study shows that distributions which range between the natural distribution and the balanced distribution can also produce the best models, contrary to the finding of the previous study. In addition, we find that the best models are larger in size than those trained using the natural distribution. We also show two different ways to achieve the same effect of the corrected probability estimates proposed by the previous study.