Tackling Class Imbalance Problem in Software Defect Prediction Through Cluster-Based Over-Sampling With Filtering

Tackling Class Imbalance Problem in Software Defect Prediction Through Cluster-Based Over-Sampling With Filtering

In practice, Software Defect Prediction (SDP) models often suffer from highly imbalanced data, which makes classifiers difficult to identify defective instances. Recently, many techniques were proposed to tackle this problem, over-sampling technique is one of the most well-known methods to address c...

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Bibliographic Details
Main Authors: Lina Gong, Shujuan Jiang, Li Jiang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Software defect prediction
over-sampling
class imbalance
K-means
noise filtering
Online Access:https://ieeexplore.ieee.org/document/8861051/

Internet

https://ieeexplore.ieee.org/document/8861051/

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