doi: 10.7763/IJCTE.2011.V3.331
Extension of Learning Vector Quantization to Cost-sensitive Learning
Ning Chen1
,
Bernardete Ribeiro2 ,
Armando Vieira1 ,
João Duarte1 ,
João C. Neves3
- 1GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto.
- 2CISUC, Department of Informatics Engineering, University of Coimbra, Portugal.
- 3ISEG, School of Economics, Technical University of Lisbon, Portugal.
Abstract
Learning vector quantization (LVQ) is an effective network model to solve classification tasks in a wide variety of real world applications. The usage of LVQ has been extended to hybrid data type. In this paper, we propose a weighted version of BNCLVQ, which incorporates the cost matrix into prototype learning and labeling by means of instance weighting. Empirical results show the superiority of proposed algorithm over original NBCLVQ and some variants on both binary-class data and multi-class data.
Keywords
- Classification
- Cost-sensitive learning
- Learning vector quantization
- Hybrid data type
How to Cite
Ning Chen, Bernardete Ribeiro, Armando Vieira, João Duarte, and João C. Neves, "Extension of Learning Vector Quantization to Cost-sensitive Learning," International Journal of Computer Theory and Engineering, vol. 3, no. 3, pp. 352-359, 2011. https://doi.org/10.7763/IJCTE.2011.V3.331
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).