C4.5: programs for machine learning
C4.5: programs for machine learning
Robust Classification for Imprecise Environments
Machine Learning
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Quantifying counts and costs via classification
Data Mining and Knowledge Discovery
Quantification and semi-supervised classification methods for handling changes in class distribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence Review
Assessing the impact of changing environments on classifier performance
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Semi-Supervised Learning
Counting positives accurately despite inaccurate classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
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In real world settings there is situation where class distribution of data may change after classifier is built resulting in performance degradation of classifier. Attempts to solve this problem from previous Class Distribution Estimation method (CDE method) yield quite interesting performance however we notice there is some flaw since CDE method still have some bias toward train data thus we decide to improve them with ensemble method. Our Class Distribution Estimation-Ensemble (CDE-EM) methods estimate class distribution from many models instead of one resulting in less bias than previous method. All methods are evaluated using accuracy on set of benchmark UCI data sets. Experimental results demonstrate that our methods yield better performance if class distribution of test data is different from train data.