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2014 On Software Defect Prediction Using Machine Learning
Jinsheng Ren, Ke Qin, Ying Ma, Guangchun Luo
J. Appl. Math. 2014: 1-8 (2014). DOI: 10.1155/2014/785435

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.

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Jinsheng Ren. Ke Qin. Ying Ma. Guangchun Luo. "On Software Defect Prediction Using Machine Learning." J. Appl. Math. 2014 1 - 8, 2014. https://doi.org/10.1155/2014/785435

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 1405.68088
MathSciNet: MR3176829
Digital Object Identifier: 10.1155/2014/785435

Rights: Copyright © 2014 Hindawi

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