earticle

영어

The knowledge consolidation model (KCM) combines the rules extracted using KBANN (NeuroRule), Frequency Matrix (which is similar to the Naïve Bayesian technique), and C5.0 algorithms. The KCM can effectively integrate multiple rule sets into one centralized knowledge base. The cumulative rules from other single models can improve overall performance as it can reduce error-term and increase R-square. The architecture of KCM also called an ensemble approach. The key idea in the KCM is to combine a number of classifiers such that the resulting combined system achieves higher classification accuracy and efficiency than the original single models (classifiers). The aim of KCM is to design a composite system that outperforms any individual classifier by pooling together the decisions of all classifiers. Compared to single models, this KCM is better in its performance as it consolidates knowledge from single models. In order to verify the feasibility and effectiveness of KCM, personal credit rating dataset provided by a local bank in Seoul, Republic of Korea is used in this study. The results from the tests show that the performance of KCM is superior to that of the other single models such as multiple discriminant analysis, logistic regression, frequency matrix, neural networks, decision trees, and NeuroRule. Moreover, our model is superior to a previous algorithm for the extraction of rules from general neural networks.