The combination of maximal margin classifiers and k-nearest neighbors rule constructing an SVM on the neighborhood of the test sample in the feature space (called kNNSVM), was presented as a promising way of improving classification accuracy. Since no extensive validation of the method was performed yet, in this work we test the kNNSVM method on 13 widely used datasets using four different kernels obtaining good classification results. Moreover we present two artificial datasets in which kNNSVM performs substantially better than SVM with RBF kernel. Statistically significant testing of the method as well as the results on the artificial datasets, lead us to conclude that kNNSVM performs sensibly better than SVM.

Empirical Assessment of Classification Accuracy of Local SVM / Segata, Nicola; Blanzieri, Enrico. - ELETTRONICO. - (2008), pp. 1-11.

Empirical Assessment of Classification Accuracy of Local SVM

Segata, Nicola
Primo
;
Blanzieri, Enrico
Ultimo
2008-01-01

Abstract

The combination of maximal margin classifiers and k-nearest neighbors rule constructing an SVM on the neighborhood of the test sample in the feature space (called kNNSVM), was presented as a promising way of improving classification accuracy. Since no extensive validation of the method was performed yet, in this work we test the kNNSVM method on 13 widely used datasets using four different kernels obtaining good classification results. Moreover we present two artificial datasets in which kNNSVM performs substantially better than SVM with RBF kernel. Statistically significant testing of the method as well as the results on the artificial datasets, lead us to conclude that kNNSVM performs sensibly better than SVM.
2008
Trento
Università degli Studi di Trento, Dipartimento di Ingegneria e Scienza dell'Informazione
Empirical Assessment of Classification Accuracy of Local SVM / Segata, Nicola; Blanzieri, Enrico. - ELETTRONICO. - (2008), pp. 1-11.
Segata, Nicola; Blanzieri, Enrico
File in questo prodotto:
File Dimensione Formato  
014.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.71 MB
Formato Adobe PDF
1.71 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/359444
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact