Abstract
Most CBR systems in operation today are ‘retrieval-only’ in that they do not adapt the solutions of retrieved cases. Adaptation is, in general, a difficult problem that often requires the acquisition and maintenance of a large body of explicit domain knowledge. For certain machine-learning tasks, however, adaptation can be performed successfully using only knowledge contained within the case base itself. One such task is regression (i.e. predicting the value of a numeric variable). This paper presents a knowledge-light regression algorithm in which the knowledge required to solve a query is generated from the differences between pairs of stored cases. Experiments show that this technique performs well relative to standard algorithms on a range of datasets.
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McDonnell, N., Cunningham, P. (2006). A Knowledge-Light Approach to Regression Using Case-Based Reasoning. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds) Advances in Case-Based Reasoning. ECCBR 2006. Lecture Notes in Computer Science(), vol 4106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11805816_9
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DOI: https://doi.org/10.1007/11805816_9
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