Hybrid system of case-based reasoning and neural network for symbolic features

Abstract

Case-based reasoning is one of the most frequently used tools in data mining. Though it has been proved to be useful in many problems, it is noted to have shortcomings such as feature weighting problems. In previous research, we proposed a hybrid system of case-based reasoning and neural network. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains. We propose another hybrid system of case-based reasoning and neural network, which uses value difference metric (VDM) for symbolic features. The proposed system is validated by datasets in symbolic domains.