Abstract
This paper proposes an algorithm to design a tree-like classifier whose result is equivalent to that achieved by the classical Nearest Neighbour rule. The procedure consists of a particular decomposition of a d-dimensional feature space into a set of convex regions with prototypes from just one class. Some experimental results over synthetic and real databases are provided in order to illustrate the applicability of the method.
This work was partially supported by grants PIB96-13 (Fundació Caixa Castelló-Bancaixa), AGF95-0712-C03-01 and TIC95-676-C02-O1 (Spanish CICYT).
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© 1998 Springer-Verlag Berlin Heidelberg
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Sánchez, J.S., Pla, F., Ferri, F.J. (1998). From the nearest neighbour rule to decision trees. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_432
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DOI: https://doi.org/10.1007/3-540-64574-8_432
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