A learning algorithm for feature weighting in case-based reasoning

Abstract

Case-based reasoning (CBR) is a methodology for problem-solving and decision making in complex business environment. This methodology is based on the reasoning of earlier experiences to solve new problems. Among well-known methods for case retrieval, K-nearest neighbour (K-NN) is more widely used. But since it is highly sensitive to input features, irrelevant features may cause an increase in the prediction error. In this research, a learning algorithm is developed by applying performance feedback to assign weight setting. The main idea behind the algorithm is to recognise cases which are incorrectly selected as nearest neighbours due to ineffective feature weighting assignment, and attempts to iteratively modify feature weights. This approach is applied to provide suggestions for better decision making and managing strategies of products in the middle of life phase. The dataset of a department store, including various products, have been used. K-fold approach is used to evaluate the prediction accuracy rate. Empirical results are compared with artificial neural network, classical CBR, CBR-GA algorithms. The results indicate that the predictive performance of the model, compare with two CBR-based algorithms, in specific case is more effective.