Who Cited It

RUSBoost: A Hybrid Approach to Alleviating Class Imbalance

2009 · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · 1,890 citations · 4 from inside this corpus

Chris Seiffert low, Taghi M. Khoshgoftaar, Jason Van Hulse low, Amri Napolitano low

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

RUSBoost: A Hybrid Approach to Alleviating Class Imbalance (2009)RUSBoost: A Hybrid Approach t…SMOTE: Synthetic Minority Over-sampling Technique (2002)SMOTE: Synthetic Minority Ove…C4.5: Programs for Machine Learning (1992)C4.5: Programs for Machine Le…UCI Repository of machine learning databases (1998)UCI Repository of machine lea…Experiments with a new boosting algorithm (1996)Experiments with a new boosti…The relationship between Precision-Recall and ROC curves (2006)The relationship between Prec…A study of the behavior of several methods for balancing machine learning training data (2004)A study of the behavior of se…Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning (2005)Borderline-SMOTE: A New Over-…Fast Effective Rule Induction (1995)Fast Effective Rule InductionA Short Introduction to Boosting (1999)A Short Introduction to Boost…The foundations of cost-sensitive learning (2001)The foundations of cost-sensi…SMOTEBoost: Improving Prediction of the Minority Class in Boosting (2003)SMOTEBoost: Improving Predict…Cost-sensitive boosting for classification of imbalanced data (2007)Cost-sensitive boosting for c…Mining with rarity (2004)Mining with rarityA Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba… (2011)A Review on Ensembles for the…Learning from class-imbalanced data: Review of methods and applications (2016)Learning from class-imbalance…Text Data Augmentation for Deep Learning (2021)Text Data Augmentation for De…An insight into classification with imbalanced data: Empirical results and current trends… (2013)An insight into classificatio…
17 of 17 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
SMOTE: Synthetic Minority Over-sampling Technique200232,402
C4.5: Programs for Machine Learning199223,704
UCI Repository of machine learning databases199810,546
Experiments with a new boosting algorithm19967,582
The relationship between Precision-Recall and ROC curves20066,427
A study of the behavior of several methods for balancing machine learning training data20044,222
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning20054,049
Fast Effective Rule Induction19953,810
A Short Introduction to Boosting19992,952
The foundations of cost-sensitive learning20011,837
SMOTEBoost: Improving Prediction of the Minority Class in Boosting20031,680
Cost-sensitive boosting for classification of imbalanced data20071,438
Mining with rarity20041,396

What cites it, inside the corpus

PaperYearCited
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…20112,834
Learning from class-imbalanced data: Review of methods and applications20162,407
Text Data Augmentation for Deep Learning20211,701
An insight into classification with imbalanced data: Empirical results and current trends…20131,602

Links

DOI · OpenAlex record

Topics

Imbalanced Data Classification TechniquesComputer Science
Machine Learning and Data ClassificationComputer Science
Electricity Theft Detection TechniquesEngineering

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports52 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

sha256 930f5bc64fea8a60…