Maintaining case-based reasoning systems: A machine learning approach

Over the years, many successful applications of case-based reasoning (CBR) systems have been developed in different areas. The performance of CBR systems depends on several factors, including case representation, similarity measure, and adaptation. Achieving good performance requires careful design, implementation, and continuous optimization of these factors. In this paper, we propose a maintenance technique that integrates an ensemble of CBR classifiers with spectral clustering and logistic regression to improve the classification accuracy of CBR classifiers on (ultra) high-dimensional biological data sets. Our proposed method is applicable to any CBR system; however, in this paper, we demonstrate the improvement achieved by applying the method to a computational framework of a CBR system called TA3.. We have evaluated the system on two publicly available microarray data sets that cover leukemia and lung cancer samples. Our maintenance method improves the classification accuracy of TA3 by approximately 20% from 65% to 79% for the leukemia and from 60% to 70% for the lung cancer data set.