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.
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Chen, N., Ribeiro, B., Vieira, A., Duarte, J., Neves, J. (2010). Weighted Learning Vector Quantization to Cost-Sensitive Learning. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_33
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DOI: https://doi.org/10.1007/978-3-642-15825-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15824-7
Online ISBN: 978-3-642-15825-4
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