Fast k-NN classifier for documents based on a graph structure
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Other documents of the author: Artigas Fuentes, Fernando; Gil García, Reynaldo; Badía, José; Pons Porrata, Aurora
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Title
Fast k-NN classifier for documents based on a graph structureDate
2010Publisher
Springer VerlagISSN
0302-9743Bibliographic citation
Lecture notes in computer science (2010), vol. 6419, p. 228-235Type
info:eu-repo/semantics/articleAbstract
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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