Journal of Applied Mathematics

On Software Defect Prediction Using Machine Learning

Jinsheng Ren, Ke Qin, Ying Ma, and Guangchun Luo

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Abstract

This paper mainly deals with how kernel method can be used for software defect prediction, since the class imbalance can greatly reduce the performance of defect prediction. In this paper, two classifiers, namely, the asymmetric kernel partial least squares classifier (AKPLSC) and asymmetric kernel principal component analysis classifier (AKPCAC), are proposed for solving the class imbalance problem. This is achieved by applying kernel function to the asymmetric partial least squares classifier and asymmetric principal component analysis classifier, respectively. The kernel function used for the two classifiers is Gaussian function. Experiments conducted on NASA and SOFTLAB data sets using F-measure, Friedman’s test, and Tukey’s test confirm the validity of our methods.

Article information

Source
J. Appl. Math. Volume 2014 (2014), Article ID 785435, 8 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
http://projecteuclid.org/euclid.jam/1425305549

Digital Object Identifier
doi:10.1155/2014/785435

Mathematical Reviews number (MathSciNet)
MR3176829

Citation

Ren, Jinsheng; Qin, Ke; Ma, Ying; Luo, Guangchun. On Software Defect Prediction Using Machine Learning. J. Appl. Math. 2014 (2014), Article ID 785435, 8 pages. doi:10.1155/2014/785435. http://projecteuclid.org/euclid.jam/1425305549.


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