On Improving Reliability of Case-based Reasoning Classifier
Zhao Hu · Acta Automatica Sinica · 2014
To aim at the reliability issue of case-based reasoning(CBR) classifier, improved strategies for case retrieve and case reuse are introduced, respectively. In the retrieve step, a new attribute weight assignment method based on the water-filling principle is proposed to optimize the feature weight; particularly, the Lagrange function is constructed by utilizing the mean value and the standard deviation of each attribute to achieve the weight result, then a weight threshold is set to conduct the attribute reduction. In the reuse step, a confidence-reuse strategy is introduced to improve the efficiency of the classifier by calculating the confidence of the target case that belongs to each class. Simulation experiments show that the proposed methods could increase the classification accuracy and efficiency, which proves that the improved strategies could effectively enhance the reliability of the CBR classifier.