This paper focuses on a bias variance decomposition analysis of a local learning algorithm, the nearest neighbor classifier, that has been extended with 'error correcting output codes'. This extended algorithm often considerably reduces the 0-1 (i.e., classification) error in comparison with nearest neigbor (Ricci & Aha, 1997). The analysis presented here reveals that this performance improvement is obtained by drastically reducing bias at the cost of increasing variance. We also show that, even in classification problems with few classes (m<_5), extending the codeword length beyond the limit that assures column separation yields an error reduction. This error reduction is not only in the variance, which is due to the voting mechanism used for error-correcting output codes, but also in the bias

Bias, Variance, and Error Correcting Output Codes for Local Learners

Ricci, Francesco;
1997

Abstract

This paper focuses on a bias variance decomposition analysis of a local learning algorithm, the nearest neighbor classifier, that has been extended with 'error correcting output codes'. This extended algorithm often considerably reduces the 0-1 (i.e., classification) error in comparison with nearest neigbor (Ricci & Aha, 1997). The analysis presented here reveals that this performance improvement is obtained by drastically reducing bias at the cost of increasing variance. We also show that, even in classification problems with few classes (m<_5), extending the codeword length beyond the limit that assures column separation yields an error reduction. This error reduction is not only in the variance, which is due to the voting mechanism used for error-correcting output codes, but also in the bias
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: http://hdl.handle.net/11582/1467
 Attenzione

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

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