RHSBoost: improving classification performance in imbalance data
type: Item
instance of: scholarly article
title: RHSBoost: improving classification performance in imbalance data
zbMATH Open document ID: 1464.62083
author: Joonho Gong
author: Hyunjoong Kim
published in: Computational Statistics and Data Analysis
publication date: 2018-08-07T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6914530
zbMATH Keywords: imbalanced data
zbMATH Keywords: ensemble
zbMATH Keywords: AUC
zbMATH Keywords: undersampling
Wikidata QID: Q57425876
describes a project that uses: R
describes a project that uses: UCI-ml
describes a project that uses: JStatCom
describes a project that uses: adabag
describes a project that uses: rpart
describes a project that uses: DMwR
describes a project that uses: C4.5
describes a project that uses: SMOTEBoost
describes a project that uses: RUSBoost
describes a project that uses: ROSE
describes a project that uses: SMOTE
MaRDI profile type: Publication
full work available at URL: j.csda.2017.01.005
OpenAlex ID: W2585770658
cites work: Q4365239
cites work: Q3327527
cites work: Q4533353
cites work: Training and assessing classification rules with imbalanced data
cites work: The effect of imbalanced data sets on LDA: a theoretical and empirical analysis
DOI: 10.1016/J.CSDA.2017.01.005
description: scientific article; zbMATH DE number 6914530