Titlebook: Artificial Intelligence in Theory and Practice II; IFIP 20th World Comp Max Bramer Conference proceedings 2008 IFIP International Federatio

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Enhancing RBF-DDA Algorithm’s Robustness: Neural Networks Applied to Prediction of Fault-Prone Softwnd (ii) to compare RBF-eDDA and MLP neural networks in software defects prediction. The simulations reported in this paper show that RBF-eDDA is able to correctly handle inconsistent patterns and that it obtains results comparable to those of MLP in the NASA data sets.

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Conference proceedings 2008formed part of the 20th World Computer Congress of IFIP, the International Federation for Information Processing (WCC-2008), in Milan, Italy in September 2008. The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (

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A Study with Class Imbalance and Random Sampling for a Decision Tree Learning Systemggest that altering the class distribution can improve the classification performance of classifiers considering AUC as a performance metric. Furthermore, as a general recommendation, random over-sampling to balance distribution is a good starting point in order to deal with class imbalance.