C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Robust Classification for Imprecise Environments
Machine Learning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Increasing sensitivity of preterm birth by changing rule strengths
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Applying One-Sided Selection to Unbalanced Datasets
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Mixture-of-Experts Framework for Learning from Imbalanced Data Sets
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hi-index | 0.00 |
Many real-world data sets exhibit skewed class distributions in which almost all instances are allotted to a class and far fewer instances to a smaller, but usually more interesting class. A classifier induced from an imbalanced data set has, characteristically, a low error rate for the majority class and an undesirable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a modification of Decorate algorithm and it concludes that such a framework can be a more valuable solution to the problem. Our method seems to permit improved identification of difficult small classes in predictive analysis, while keeping the classification ability of the majority class in an acceptable level.