Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Application of rule induction and rough sets to verification of magnetic resonance diagnosis
Fundamenta Informaticae
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
Selective Pre-processing of Imbalanced Data for Improving Classification Performance
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
An attribute reduct algorithm based on clustering
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice
Applied Soft Computing
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The paper addresses problems of improving performance of rule-based classifiers constructed from imbalanced data sets, i.e., data sets where the minority class of primary importance is under-represented in comparison to majority classes. We introduced two techniques to detect and process inconsistent examples from the majority classes in the boundary between the minority and majority classes. Both these techniques differ in the way of processing inconsistent boundary examples from the majority classes. The first approach removes them, while the other relabels them as belonging to the minority class. The experiments showed that the best results were obtained for the filtering technique, where inconsistent majority class examples were reassigned to the minority class, combined with a classifier composed of decision rules generated by the MODLEM algorithm.