Instance and feature weighted k-nearest-neighbors algorithm
Tipo de documentoTexto en actas de congreso
Fecha de publicación2016
Condiciones de accesoAcceso abierto
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.
CitaciónPrat, G., Belanche, Ll. Instance and feature weighted k-nearest-neighbors algorithm. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". Bruges: I6doc.com, 2016, p. 605-610.
Versión del editorhttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-178.pdf