Fast k-NN classifier for documents based on a graph structure
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Otros documentos de la autoría: Artigas Fuentes, Fernando; Gil García, Reynaldo; Badía, José; Pons Porrata, Aurora
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Título
Fast k-NN classifier for documents based on a graph structureFecha de publicación
2010Editor
Springer VerlagISSN
0302-9743Cita bibliográfica
Lecture notes in computer science (2010), vol. 6419, p. 228-235Tipo de documento
info:eu-repo/semantics/articlePalabras clave / Materias
Resumen
In this paper, a fast k nearest neighbors (k-NN) classifier for documents is presented. Documents are usually represented in a high-dimensional feature space, where terms appeared on it are treated as features and the ... [+]
In this paper, a fast k nearest neighbors (k-NN) classifier for documents is presented. Documents are usually represented in a high-dimensional feature space, where terms appeared on it are treated as features and the weight of each term reflects its importance in the document. There are many approaches to find the vicinity of an object, but their performance drastically decreases as the number of dimensions grows. This problem prevents its application for documents. The proposed method is based on a graph index structure with a fast search algorithm. It’s high selectivity permits to obtain a similar classification quality than exhaustive classifier, with a few number of computed distances. Our experimental results show that it is feasible the use of the proposed method in problems of very high dimensionality, such as Text Mining. [-]
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