Abstract
When Case Based Reasoning systems are applied to real-world problems, the retrieved solutions in general require adaptations in order to be useful in new contexts. Therefore, case adaptation is a desirable capability of Case Based Reasoning systems. However, case adaptation is still a challenge for this research area. In general, the acquisition of knowledge for adaptation is more complex than the acquisition of cases. This paper explores the use of a hybrid committee of Machine Learning techniques for automatic case adaptation.
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Policastro, C.A., de Carvalho, A.C.P.L.F., Delbem, A.C.B. (2004). Automatic Case Adaptation with a Hybrid Committee Approach. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_31
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DOI: https://doi.org/10.1007/978-3-540-24694-7_31
Publisher Name: Springer, Berlin, Heidelberg
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