Kernel Based Asymmetric Learning for Software Defect Prediction

Ying MA  Guangchun LUO  Hao CHEN  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.1   pp.267-270
Publication Date: 2012/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.267
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Software Engineering
Keyword: 
defect prediction,  class imbalance,  kernel principal component analysis,  machine learning,  

Full Text: PDF(73KB)>>
Buy this Article




Summary: 
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.