Induction of fuzzy decision trees
Fuzzy Sets and Systems
A new version of the rule induction system LERS
Fundamenta Informaticae
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Information Sciences—Informatics and Computer Science: An International Journal
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The class imbalance problem: A systematic study
Intelligent Data Analysis
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
FRCT: fuzzy-rough classification trees
Pattern Analysis & Applications
On the compact computational domain of fuzzy-rough sets
Pattern Recognition Letters
Weighted rough set learning: towards a subjective approach
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice
Applied Soft Computing
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
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Rough set theory is an efficient tool for machine learning and knowledge acquisition. By introducing weightiness into a fuzzy approximation space, a new rule induction algorithm is proposed, which combines three types of uncertainty: weightiness, fuzziness and roughness. We first define the key concepts of block, minimal complex and local covering in a weighted fuzzy approximation space, then a weighted fuzzy approximation space based rule learner, and finally a weighted certainty factor for evaluating fuzzy classification rules. The time complexity of proposed rule learner is theoretically analyzed. Furthermore, in order to estimate the performance of the proposed method on class imbalanced and hybrid datasets, we compare our method with classical methods by conducting experiments on fifteen datasets. Comparative studies indicate that rule sets extracted by this method get a better performance on minority class than other approaches. It is therefore concluded that the proposed rule learner is an effective method for class imbalanced and hybrid data learning.