Abstract
This paper proposes a genetic-algorithm-based approach for finding a compact reference set used in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A binary string is used for representing the inclusion (or exclusion) of each pattern and feature in the reference set. Our goal is to minimize the number of selected patterns, to minimize the number of selected features, and to maximize the classification performance of the reference set. The effectiveness of our approach is examined by computer simulations on commonly used data sets.
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© 1999 Springer-Verlag Berlin Heidelberg
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Ishibuchi, H., Nakashima, T. (1999). Evolution of Reference Sets in Nearest Neighbor Classification. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_12
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DOI: https://doi.org/10.1007/3-540-48873-1_12
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