Contributions of domain knowledge and stacked generalization in AI-based classification models

Weiping Kostenko, Vincent Lee, Ting Tan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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.
Original languageEnglish
Title of host publicationAI 2004: Advances in Artificial Intelligence
Subtitle of host publication17th Australian Joint Conference on Artificial Intelligence Cairns, Australia, December 4-6, 2004 Proceedings
EditorsGeoffrey I. Webb, Xinghuo Yu
Place of PublicationBerlin Germany
Number of pages6
ISBN (Print)3540240594
Publication statusPublished - 2004
EventAustralasian Joint Conference on Artificial Intelligence 2004 - Cairns, Australia
Duration: 4 Dec 20046 Dec 2004
Conference number: 17th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceAustralasian Joint Conference on Artificial Intelligence 2004
Abbreviated titleAI 2004
Internet address

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