Abstract
Most case-based reasoning systems in operation today do not adapt the solutions of retrieved cases to solve new problems. This reflects the difficulty of acquiring and maintaining the knowledge needed to perform adaptation successfully. This paper describes a technique for performing numeric prediction (i.e. regression) in which adaptation knowledge is mined from the case-base itself. Experimental evidence suggests that the technique provides robust performance in real-world domains. © 2006 Springer-Verlag London.
Cite
CITATION STYLE
McDonnell, N., & Cunningham, P. (2006). Using case differences for regression in CBR systems. In Research and Development in Intelligent Systems XXII - Proceedings of AI 2005, the 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 219–232). Springer London. https://doi.org/10.1007/978-1-84628-226-3_17
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