Asymmetric learning based on kernel partial least squares for software defect prediction

Guangchun Luo; Ying Ma; Ke Qin

doi:10.1587/transinf.E95.D.2006

Asymmetric learning based on kernel partial least squares for software defect prediction

Guangchun Luo^*
, Ying Ma
, Ke Qin

^*Corresponding author for this work

University of Electronic Science and Technology of China

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

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.

Original language	English
Pages (from-to)	2006-2008
Number of pages	3
Journal	IEICE Transactions on Information and Systems
Volume	E95-D
Issue number	7
DOIs	https://doi.org/10.1587/transinf.E95.D.2006
State	Published - Jul 2012
Externally published	Yes

Keywords

Class imbalance
Defect prediction
Kernel partial least squares
Machine learning

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10.1587/transinf.E95.D.2006

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title = "Asymmetric learning based on kernel partial least squares for software defect prediction",

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.",

keywords = "Class imbalance, Defect prediction, Kernel partial least squares, Machine learning",

author = "Guangchun Luo and Ying Ma and Ke Qin",

year = "2012",

month = jul,

doi = "10.1587/transinf.E95.D.2006",

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volume = "E95-D",

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AU - Ma, Ying

AU - Qin, Ke

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N2 - 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.

AB - 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.

KW - Class imbalance

KW - Defect prediction

KW - Kernel partial least squares

KW - Machine learning

UR - https://www.scopus.com/pages/publications/84863490485

U2 - 10.1587/transinf.E95.D.2006

DO - 10.1587/transinf.E95.D.2006

M3 - 文章

AN - SCOPUS:84863490485

SN - 0916-8532

VL - E95-D

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EP - 2008

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

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ER -

Asymmetric learning based on kernel partial least squares for software defect prediction

Abstract

Keywords

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