Data Mining and Knowledge Discovery
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
The class imbalance problem: A systematic study
Intelligent Data Analysis
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Weighted reduction for decision tables
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
Compact Rule Learner on Weighted Fuzzy Approximation Spaces for Class Imbalanced and Hybrid Data
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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Classical rough set theory has shown powerful capability in attribute dependence analysis, knowledge reduction and decision rule extraction. However, in some applications where the subjective and apriori knowledge must be considered, such as cost-sensitive learning and class imbalance learning, classical rough set can not obtain the satisfying results due to the absence of a mechanism of considering the subjective knowledge. This paper discusses problems connected with introducing the subjective knowledge into rough set learning and proposes a weighted rough set learning approach. In this method, weights are employed to represent the subjective knowledge and a weighted information system is defined firstly. Secondly, attribute dependence analysis under the subjective knowledge is performed and weighted approximate quality is given. Finally, weighted attribute reduction algorithm and weighted rule extraction algorithm are designed. In order to validate the proposed approach, experimentations of class imbalance learning and cost-sensitive learning are constructed. The results show that the introduction of appropriate weights can evidently improve the performance of rough set learning, especially, increasing the accuracy of the minority class and the AUC for class imbalance learning and decreasing the classification cost for cost-sensitive learning.