Neural Networks
Selected papers of the sixth annual Oregon workshop on Software metrics
A field study of scale economies in software maintenance
Management Science - Special issue: Frontier research on information systems and economics
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Randomized Variable Elimination
The Journal of Machine Learning Research
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Journal of Systems and Software - Special issue: Quality software
Journal of Systems and Software - Special issue: Quality software
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
A threshold varying bisection method for cost sensitive learning in neural networks
Expert Systems with Applications: An International Journal
How good is your blind spot sampling policy
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
IEEE Transactions on Neural Networks
A hybrid radial basis function and data envelopment analysis neural network for classification
Computers and Operations Research
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In this paper, we propose a software defect prediction model learning problem (SDPMLP) where a classification model selects appropriate relevant inputs, from a set of all available inputs, and learns the classification function. We show that the SDPMLP is a combinatorial optimization problem with factorial complexity, and propose two hybrid exhaustive search and probabilistic neural network (PNN), and simulated annealing (SA) and PNN procedures to solve it. For small size SDPMLP, exhaustive search PNN works well and provides an (all) optimal solution(s). However, for large size SDPMLP, the use of exhaustive search PNN approach is not pragmatic and only the SA-PNN allows us to solve the SDPMLP in a practical time limit. We compare the performance of our hybrid approaches with traditional classification algorithms and find that our hybrid approaches perform better than traditional classification algorithms.