Abstract
We present a novel approach to classification, based on a tight coupling of instance-based learning and a genetic algorithm. In contrast to the usual instance-based learning setting, we do not rely on (parts of) the given training set as the basis of a nearest-neighbor classifier, but we try to employ artificially generated instances as concept prototypes. The extremely hard problem of finding an appropriate set of concept prototypes is tackled by a genetic search procedure with the classification accuracy on the given training set as evaluation criterion for the genetic fitness measure. Experiments show that—due to the ability to find concise and accurate concept descriptions that contain few, but typical instances—this classification approach is considerably robust against noise, untypical training instances and irrelevant attributes.
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Fuchs, M., Abecker, A. (1996). Optimized nearest-neighbor classifiers using generated instances. In: Görz, G., Hölldobler, S. (eds) KI-96: Advances in Artificial Intelligence. KI 1996. Lecture Notes in Computer Science, vol 1137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61708-6_49
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DOI: https://doi.org/10.1007/3-540-61708-6_49
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