C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Communications of the ACM
Uncertainly measures of rough set prediction
Artificial Intelligence
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
The algorithm on knowledge reduction in incomplete information systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
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
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Information Sciences: an International Journal - Special issue: Medical expert systems
Entropies of fuzzy indiscrenibility relation and its operations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction
Fundamenta Informaticae
Rough Sets for Handling Imbalanced Data: Combining Filtering and Rule-based Classifiers
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
The class imbalance problem: A systematic study
Intelligent Data Analysis
Analysis on classification performance of rough set based reducts
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
Fundamenta Informaticae
Generalized fuzzy rough sets determined by a triangular norm
Information Sciences: an International Journal
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Communicating between information systems
Information Sciences: an International Journal
A short note on algebraic T-rough sets
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
A comparative study on rough set based class imbalance learning
Knowledge-Based 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
Information Sciences: an International Journal
Dominance-based rough set approach to incomplete interval-valued information system
Data & Knowledge Engineering
Information Sciences: an International Journal
New roughness measures of the interval-valued fuzzy sets
Expert Systems with Applications: An International Journal
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
Comparing alternative classifiers for database marketing: The case of imbalanced datasets
Expert Systems with Applications: An International Journal
A class of rough multiple objective programming and its application to solid transportation problem
Information Sciences: an International Journal
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice
Applied Soft Computing
Identifying the medical practice after total hip arthroplasty using an integrated hybrid approach
Computers in Biology and Medicine
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
Quick attribute reduction in inconsistent decision tables
Information Sciences: an International Journal
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In this paper, we introduce weights into Pawlak rough set model to balance the class distribution of a data set and develop a weighted rough set based method to deal with the class imbalance problem. In order to develop the weighted rough set based method, we design first a weighted attribute reduction algorithm by introducing and extending Guiasu weighted entropy to measure the significance of an attribute, then a weighted rule extraction algorithm by introducing a weighted heuristic strategy into LEM2 algorithm, and finally a weighted decision algorithm by introducing several weighted factors to evaluate extracted rules. Furthermore, in order to estimate the performance of the developed method, we compare the weighted rough set based method with several popular methods used for class imbalance learning by conducting experiments with twenty UCI data sets. Comparative studies indicate that in terms of AUC and minority class accuracy, the weighted rough set based method is better than the re-sampling and filtering based methods, and is comparable to the decision tree and SVM based methods. It is therefore concluded that the weighted rough set based method is effective for class imbalance learning.