RHSBoost: improving classification performance in imbalance data

DOI10.1016/j.csda.2017.01.005zbMath1464.62083OpenAlexW2585770658WikidataQ57425876 ScholiaQ57425876MaRDI QIDQ1654228

Hyunjoong Kim, Joonho Gong

Publication date: 7 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2017.01.005

zbMATH Keywords

AUCensembleimbalanced dataundersampling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

A Novel Hybrid Sampling Algorithm for Solving Class Imbalance Problem in Big Data ⋮ AECID: asymmetric entropy for classifying imbalanced data ⋮ RHSBoost

Uses Software

• R
• UCI-ml
• JStatCom
• rpart
• adabag
• DMwR
• C4.5
• SMOTEBoost
• RUSBoost
• ROSE
• SMOTE

Cites Work

• The effect of imbalanced data sets on LDA: a theoretical and empirical analysis
• Training and assessing classification rules with imbalanced data
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