Abstract
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.
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Wu, W., Lee, V.C., Tan, T. (2004). Contributions of Domain Knowledge and Stacked Generalization in AI-Based Classification Models. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_100
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DOI: https://doi.org/10.1007/978-3-540-30549-1_100
Publisher Name: Springer, Berlin, Heidelberg
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