A hybrid classification method using error pattern modeling

This paper presents a new hybrid classification method using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method tries to increase prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by two methods, and the extracted data subset is used to learn when each method works better. The learned discrimination model is called error pattern models and is used to merge the prediction results of two different methods to generate final prediction. The proposed method has been tested using 13 real-world data sets. The analysis results show that the performance of the proposed method is superior to other hybrid methods and the single usage of existing classification methods such as artificial neural networks and decision tree induction. In particular when prediction inconsistency ratio of the two methods is high, the proposed hybrid method provides significant improvement of prediction accuracy.