Solving of optimization and identification problems by the committee methods
Pattern Recognition
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Software metrics for reliability assessment
Handbook of software reliability engineering
Tree-Based Software Quality Estimation Models For Fault Prediction
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Classification Tree Models of Software Quality Over Multiple Releases
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
IEEE Transactions on Neural Networks
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Software quality prediction models seek to predict quality factors such as whether a component is fault prone or not. This can be treated as a kind of pattern recognition problem. In pattern recognition, there is a growing use of multiple classifier combinations with the goal to increase recognition performance. In this paper, we propose a neural network approach to combine multiple classifiers. The combination network consists of two neural networks: a Kohonen self-organization network and a multilayer perceptron network. The multilayer perceptron network is used as Dynamic Selection Network (DSN) and Kohonen self-organization network is served as the final combiner. A case study illustrates our approach and provides the evidence that the combination network with DSN performs better than some other popular combining schemes and the DSN can efficiently improve the performance of the combination network.