A classification approach using multi-layered neural networks
Decision Support Systems - Special issue on neural networks for decision support
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data Representation for Diagnostic Neural Networks
IEEE Expert: Intelligent Systems and Their Applications
Empirical Study of a Stacking State-space
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
NeC4.5: Neural Ensemble Based C4.5
IEEE Transactions on Knowledge and Data Engineering
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We exploit the merits of C4.5 decision tree classifier with two stacking meta-learners: back-propagation multilayer perceptron neural network and naive-Bayes respectively The performance of these two hybrid classification schemes have been empirically tested and compared with C4.5 decision tree using two US data sets (raw data set and new data set incorporated with domain knowledge) simultaneously to predict US bank failure Significant improvements in prediction accuracy and training efficiency have been achieved in the schemes based on new data set The empirical test results suggest that the proposed hybrid schemes perform marginally better in term of AUC criterion.