J. Jarmulak and S. Craw, “Genetic Algorithms for Feature Selection and Weighting”, in: IJCAI-99 Workshop on Automating the Construction of Case-Based Reasoners, Stockholm, Sweden, 2 August 1999, S.S. Anand, A. Aamodt and D.W. Aha (eds.), pp. 28-33. (pdf)
Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the case-base, and feature weighting methods that improve the similarity-based selection of relevant cases. Two main types of method are presented: filter methods use no feedback from the learning algorithm that will be applied; wrapper methods incorporate feedback and hence take account of learning bias. Wrapper methods based on Genetic Algorithms have been found to deliver the best results with a tablet design application, but these generic methods are flexible about the criterion to be optimised, and should be applicable to a wide variety of problems.