Abstract
An asymmetric classifier based on kernel partial least squares is proposed for software defect prediction. This method improves the prediction performance on imbalanced data sets. The experimental results validate its effectiveness. © 2012 The Institute of Electronics, Information and Communication Engineers.
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APA
Luo, G., Ma, Y., & Qin, K. (2012). Asymmetric learning based on kernel partial least squares for software defect prediction. In IEICE Transactions on Information and Systems (Vol. E95-D, pp. 2006–2008). Institute of Electronics, Information and Communication, Engineers, IEICE. https://doi.org/10.1587/transinf.E95.D.2006
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