Kernel based asymmetric learning for software defect prediction

Ying Ma; Guangchun Luo; Hao Chen

doi:10.1587/transinf.E95.D.267

Kernel based asymmetric learning for software defect prediction

Ying Ma^*
, Guangchun Luo
, Hao Chen

^*Corresponding author for this work

University of Electronic Science and Technology of China

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

15 Scopus citations

Abstract

A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.

Original language	English
Pages (from-to)	267-270
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E-95-D
Issue number	1
DOIs	https://doi.org/10.1587/transinf.E95.D.267
State	Published - Jan 2012
Externally published	Yes

Keywords

Class imbalance
Defect prediction
Kernel principal component analysis
Machine learning

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10.1587/transinf.E95.D.267

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title = "Kernel based asymmetric learning for software defect prediction",

abstract = "A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.",

keywords = "Class imbalance, Defect prediction, Kernel principal component analysis, Machine learning",

author = "Ying Ma and Guangchun Luo and Hao Chen",

year = "2012",

month = jan,

doi = "10.1587/transinf.E95.D.267",

language = "英语",

volume = "E-95-D",

pages = "267--270",

journal = "IEICE Transactions on Information and Systems",

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T1 - Kernel based asymmetric learning for software defect prediction

AU - Ma, Ying

AU - Luo, Guangchun

AU - Chen, Hao

PY - 2012/1

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N2 - A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.

AB - A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.

KW - Class imbalance

KW - Defect prediction

KW - Kernel principal component analysis

KW - Machine learning

UR - https://www.scopus.com/pages/publications/84855298554

U2 - 10.1587/transinf.E95.D.267

DO - 10.1587/transinf.E95.D.267

M3 - 文章

AN - SCOPUS:84855298554

SN - 0916-8532

VL - E-95-D

SP - 267

EP - 270

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

IS - 1

ER -

Kernel based asymmetric learning for software defect prediction

Abstract

Keywords

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