Case-based reasoning and neural network based expert system for personalization
We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. 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 feature-weighted CBR with neural network, which uses value difference metric (VDM) as distance function for symbolic features. In our system, the feature weight set calculated from the trained neural network plays the core role in connecting both the learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. To validate our system, illustrative experimental results are presented. We use datasets from the UCI machine learning archive for experiments. Finally, we present an application with a personalized counseling system for cosmetic industry whose questionnaires have symbolic features. Feature-weighted CBR with neural network predicts the five elements, which show customers character and physical constitution, with relatively high accuracy and expert system for personalization recommends personalized make-up style, color, life style and products.
case-based reasoning neural network personalization machine learning cosmetic industry knowledge base