A random approximate reduct-based ensemble learning approach and its application in software defect prediction

DOI10.1016/j.ins.2022.07.130OpenAlexW4288045490WikidataQ113872289 ScholiaQ113872289MaRDI QIDQ6122240

Junwei Du, Xu Yu, Feng Jiang, Dun-wei Gong

Publication date: 27 March 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2022.07.130

zbMATH Keywords

rough setsensemble learningclass imbalancesoftware defect predictiongranular decision entropyrandom approximate reduct

Mathematics Subject Classification ID

Computer science (68-XX) Operations research, mathematical programming (90-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A Mathematical Theory of Communication
• EROS: Ensemble rough subspaces
• Feature analysis through information granulation and fuzzy sets
• A relative decision entropy-based feature selection approach
• A hybrid data-level ensemble to enable learning from highly imbalanced dataset
• Ensemble learning from model based trees with application to differential price sensitivity assessment
• Decision bireducts and decision reducts -- a comparison
• Feature selection via max-independent ratio and min-redundant ratio based on adaptive weighted kernel density estimation

This page was built for publication: A random approximate reduct-based ensemble learning approach and its application in software defect prediction