Título:
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Instance and feature weighted k-nearest-neighbors algorithm
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Autor/a:
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Prat, Gabriel; Belanche Muñoz, Luis Antonio
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract:
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We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process -assigning different importances to instances as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in a standard Nearest-Neighbours classifier.
We report extensive experimentation in well-known benchmarking datasets as well as some challenging microarray
gene expression problems. Our results show increases in stability for most subset sizes and most problems, without
compromising prediction accuracy. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Neural networks (Computer science) -Gene expression -Feature subset -Feature weighting -Instance weighting -Microarray gene expression -Nearest neighbour -Pre-processing step -Prediction accuracy -Training process -Xarxes neuronals (Informàtica) |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Objeto de conferencia |
Editor:
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I6doc.com
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