Weighted learning vector quantization to cost-sensitive learning

Authors:
Ning Chen;Bernardete Ribeiro;Armando Vieira;João Duarte;João Neves
Affiliations:
GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto;CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto;GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto;ISEG, School of Economics, Technical University of Lisbon, Portugal
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

Citing 6
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Abstract

The importance of cost-sensitive learning becomes crucial when the costs of misclassifications are quite different. Many evidences have demonstrated that a cost-sensitive predictive model is more desirable in practical applications than a traditional one without taking the cost into consideration. In this paper, we propose two approaches which incorporate the cost matrix into original learning vector quantization by means of instance weighting. Empirical results show that the proposed algorithms are effective on both binary-class data and multi-class data.