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Title of Journal: Optimization Letters

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Abbravation: Optimization Letters

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Springer-Verlag

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DOI

10.1016/0887-6177(95)92922-r

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1862-4480

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A comparison of several nearest neighbor classifie

Authors: Magdalene Marinaki Yannis Marinakis Michael Doumpos Nikolaos Matsatsinis Constantin Zopounidis
Publish Date: 2007/07/13
Volume: 2, Issue: 3, Pages: 299-308
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Abstract

The feature selection problem is an interesting and important topic which is relevant for a variety of database applications This paper utilizes the Tabu Search metaheuristic algorithm to implement a feature subset selection procedure while the nearest neighbor classification method is used for the classification task Tabu Search is a general metaheuristic procedure that is used in order to guide the search to obtain good solutions in complex solution spaces Several metrics are used in the nearest neighbor classification method such as the euclidean distance the Standardized Euclidean distance the Mahalanobis distance the City block metric the Cosine distance and the Correlation distance in order to identify the most significant metric for the nearest neighbor classifier The performance of the proposed algorithms is tested using various benchmark datasets from UCI Machine Learning Repository

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