Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Software Engineering: An Engineering Approach
Software Engineering: An Engineering Approach
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Machine Learning
Towards a Software Change Classification System: A Rough Set Approach
Software Quality Control
An Application of Fuzzy Clustering to Software Quality Prediction
ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
IEEE Transactions on Software Engineering
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Refinement of generated fuzzy production rules by using a fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Knowledge discovery methods used to find relationships among software engineering data and the extraction of rules have gained increasing importance in recent years. These methods have become necessary for improvements in the quality of the software product and the process. The focus of this paper is a first attempt towards combining strengths of rough set theory and neuro-fuzzy decision trees in classifying software defect data. We compare classification results for four methods: rough sets, neuro-fuzzy decision trees, partial decision trees, rough-neuro-fuzzy decision trees. The analysis of the results include a family-wise 10 fold paired t-test for accuracy and number of rules. The contribution of this paper is the application of a hybrid rough-neuro-fuzzy decision tree method in classifying software defect data.