IEICE TRANSACTIONS on Information
Kernel CCA Based Transfer Learning for Software Defect Prediction
Summary :
An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.8 pp.1903-1906
- Publication Date
- 2017/08/01
- Publicized
- 2017/04/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDL8238
- Type of Manuscript
- LETTER
- Category
- Software Engineering
Authors
Ying MA
Xiamen University of Technology
Shunzhi ZHU
Xiamen University of Technology
Yumin CHEN
Xiamen University of Technology
Jingjing LI
University of Electronic Science and Technology of China
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Ying MA, Shunzhi ZHU, Yumin CHEN, Jingjing LI, "Kernel CCA Based Transfer Learning for Software Defect Prediction" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 8, pp. 1903-1906, August 2017, doi: 10.1587/transinf.2016EDL8238.
Abstract: An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8238/_p
Copy
@ARTICLE{e100-d_8_1903,
author={Ying MA, Shunzhi ZHU, Yumin CHEN, Jingjing LI, },
journal={IEICE TRANSACTIONS on Information},
title={Kernel CCA Based Transfer Learning for Software Defect Prediction},
year={2017},
volume={E100-D},
number={8},
pages={1903-1906},
abstract={An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.},
keywords={},
doi={10.1587/transinf.2016EDL8238},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Kernel CCA Based Transfer Learning for Software Defect Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 1903
EP - 1906
AU - Ying MA
AU - Shunzhi ZHU
AU - Yumin CHEN
AU - Jingjing LI
PY - 2017
DO - 10.1587/transinf.2016EDL8238
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2017
AB - An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.
ER -
English
