Carrizosa Priego, Emilio JoséMartín Barragán, BelénPlastria, FrankRomero Morales, María Dolores2021-04-262021-04-262007-07-20Carrizosa Priego, E.J., Martín Barragán, B., Plastria, F. y Romero Morales, M.D. (2007). On the Selection of the Globally Optimal Prototype Subset for Nearest-Neighbor Classification. INFORMS JOURNAL ON COMPUTING, 19 (3), 470-479.1091-98561526-5528https://hdl.handle.net/11441/107714The nearest-neighbor classifier has been shown to be a powerful tool for multiclass classification. We explore both theoretical properties and empirical behavior of a variant method, in which the nearest-neighbor rule is applied to a reduced set of prototypes. This set is selected a priori by fixing its cardinality and minimizing the empirical misclassification cost. In this way we alleviate the two serious drawbacks of the nearest-neighbor method: high storage requirements and time-consuming queries. Finding this reduced set is shown to be NP-hard. We provide mixed integer programming (MIP) formulations, which are theoretically compared and solved by a standard MIP solver for small problem instances. We show that the classifiers derived from these formulations are comparable to benchmark procedures. We solve large problem instances by a metaheuristic that yields good classification rules in reasonable time. Additional experiments indicate that prototype-based nearest-neighbor classifiers remain quite stable in the presence of missing values.application/pdf9 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/classificationoptimal prototype subsetnearest neighbordissimilaritiesinteger programmingvariable neighborhood searchmissing valuesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1287/ijoc.1060.0183