In this paper we test a new multicategory SVM method, called Augmented Binary (AB), on microarray gene expression data. The AB SVM is one of the methods generating a multicategory classifier in one step, without dividing the multiclass problem into binary subproblems. This approach can be useful when the number of samples is very low, like in this kind of application. Furthermore, the use of a single SVM, instead of several binary ones, simplifies the search for optimal hyperparameters and allows a consistent output for all the classes.

Testing the Augmented Binary Multiclass SVM on Microarray Data

ANGUITA, DAVIDE;RIDELLA, SANDRO;
2006

Abstract

In this paper we test a new multicategory SVM method, called Augmented Binary (AB), on microarray gene expression data. The AB SVM is one of the methods generating a multicategory classifier in one step, without dividing the multiclass problem into binary subproblems. This approach can be useful when the number of samples is very low, like in this kind of application. Furthermore, the use of a single SVM, instead of several binary ones, simplifies the search for optimal hyperparameters and allows a consistent output for all the classes.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11567/315642
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact