Software Defect Prediction Based on Data Sampling and Multivariate Filter Feature Selection

DOI

10.2991/icaita-18.2018.33How to use a DOI?

Keywords

software defect prediction; data sampling; multivariate Filter algorithm

Abstract

In order to solve the useless feature and class imbalance problem in software defect prediction(SDP), this paper proposes a new prediction method which is based on data sampling and multivariate filter feature selection. Firstly, the sampling method re-samples the data set to achieve the data balance. Secondly, the multivariate filter algorithm selects feature and eliminates useless features such as irrelevant features and redundant features. Experimental results show that the proposed algorithm not only can effectively improve the prediction accuracy of the minority classes, but also effectively improve the overall classification performance of SDP.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yating Lin
AU  - Yiwen Zhong
PY  - 2018/03
DA  - 2018/03
TI  - Software Defect Prediction Based on Data Sampling and Multivariate Filter Feature Selection
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 128
EP  - 131
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.33
DO  - 10.2991/icaita-18.2018.33
ID  - Lin2018/03
ER  -