Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper p...

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Bibliographic Details
Main Authors: Haitao He, Xu Zhang, Qian Wang, Jiadong Ren, Jiaxin Liu, Xiaolin Zhao, Yongqiang Cheng
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Software defect prediction
class imbalance
combined sampling
rule learning
MultiBoost
Online Access:https://ieeexplore.ieee.org/document/8793088/

Internet

https://ieeexplore.ieee.org/document/8793088/

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