Hybrid case-based reasoning system by cost-sensitive neural network for classification

Abstract

Case-based reasoning (CBR) is an artificial intelligent approach to learning and problem-solving, which solves a target problem by relating past similar solved problems. But it faces the challenge of weights assignment to features to measure similarity between cases. There are many methods to overcome this feature weighting problem of CBR. However, neural network’s pruning is one of the powerful and useful methods to overcome this feature weighting problem, which extracts feature weights from trained neural network without losing the generality of training set by four popular mechanisms: sensitivity, activity, saliency and relevance. It is habitually assumed that the training sets used for learning are balanced. However, this hypothesis is not always true in real-world applications, and hence, the tendency is to yield classification models that are biased toward the overrepresented class. Therefore, a hybrid CBR system is proposed in this paper to overcome this problem, which adopts a cost-sensitive back-propagation neural network (BPNN) in network pruning to find feature weights. These weights are used in CBR. A single cost parameter is used by the cost-sensitive BPNN to distinguish the importance of class errors. A balanced decision boundary is generated by the cost parameter using prior information. Thus, the class imbalance problem of network pruning is overcome to improve the accuracy of the hybrid CBR. From the empirical results, it is observed that the performance of the proposed hybrid CBR system is better than the hybrid CBR by standard neural network. The performance of the proposed hybrid system is validated with seven datasets.