Cost-sensitive learning is of critical importance in many domains including bankruptcy prediction where the costs of different errors are unequal. Most existing classification methods aim to minimize overall error based on the assumption that the costs are equal. This paper presents three cost-sensitive learning vector quantization (LVQ) approaches to incorporate cost matrix in classification. Experimental results on real-world data indicate the proposed approaches are effective alternatives for bankruptcy prediction in cost-sensitive situations.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.