TY - GEN
T1 - An Under-sampling Method
T2 - 2nd IEEE International Conference on Software Quality, Reliability and Security-Companion, QRS-C 2016
AU - Fu, Yangzhen
AU - Zhang, Hong
AU - Bai, Yaxin
AU - Sun, Weixuan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/21
Y1 - 2016/9/21
N2 - Machine learning method can be used to forecast software fault, and identify modules which have the tendency to cause soft-error at the early life cycle, then software developer can modify these defect modules early. It has an important significance on the improvement of software reliability. However, fault samples of software data sets are smaller in number (one or two order of magnitudes) compared with fault-free samples, learning machine's predictive ability to fault samples has been restrained by this kind of unbalanced data sets. This paper put forward an under-sampling method based on principal component analysis (PCA) and comprehensive evaluation model to get rid of redundant majority class samples under the premise of conserving data of majority class characteristic as far as possible, and reaches to a balance between this two kinds of samples.
AB - Machine learning method can be used to forecast software fault, and identify modules which have the tendency to cause soft-error at the early life cycle, then software developer can modify these defect modules early. It has an important significance on the improvement of software reliability. However, fault samples of software data sets are smaller in number (one or two order of magnitudes) compared with fault-free samples, learning machine's predictive ability to fault samples has been restrained by this kind of unbalanced data sets. This paper put forward an under-sampling method based on principal component analysis (PCA) and comprehensive evaluation model to get rid of redundant majority class samples under the premise of conserving data of majority class characteristic as far as possible, and reaches to a balance between this two kinds of samples.
KW - PCA
KW - Under-Sampling
KW - comprehensive evaluation
UR - https://www.scopus.com/pages/publications/84991757124
U2 - 10.1109/QRS-C.2016.68
DO - 10.1109/QRS-C.2016.68
M3 - 会议稿件
AN - SCOPUS:84991757124
T3 - Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security-Companion, QRS-C 2016
SP - 414
EP - 415
BT - Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security-Companion, QRS-C 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 August 2016 through 3 August 2016
ER -