Who Cited It

A study of the behavior of several methods for balancing machine learning training data

2004 · ACM SIGKDD Explorations Newsletter · 4,222 citations · 10 from inside this corpus

Gustavo E. A. P. A. Batista low, Ronaldo C. Prati, Maria Carolina Monard

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.

A study of the behavior of several methods for balancing machine learning training data (2004)A study of the behavior of se…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…Instance-Based Learning Algorithms (1991)Instance-Based Learning Algor…The class imbalance problem: A systematic study1 (2002)The class imbalance problem: …An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia… (1999)An Empirical Comparison of Vo…Learning from Imbalanced Data (2009)Learning from Imbalanced DataBorderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning (2005)Borderline-SMOTE: A New Over-…A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba… (2011)A Review on Ensembles for the…Exploratory Undersampling for Class-Imbalance Learning (2008)Exploratory Undersampling for…SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann… (2018)SMOTE for Learning from Imbal…Editorial (2004)EditorialRUSBoost: A Hybrid Approach to Alleviating Class Imbalance (2009)RUSBoost: A Hybrid Approach t…CLASSIFICATION OF IMBALANCED DATA: A REVIEW (2009)CLASSIFICATION OF IMBALANCED …An insight into classification with imbalanced data: Empirical results and current trends… (2013)An insight into classificatio…Cost-sensitive boosting for classification of imbalanced data (2007)Cost-sensitive boosting for c…
16 of 16 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
Instance-Based Learning Algorithms19914,128
The class imbalance problem: A systematic study120023,353
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…19992,656

What cites it, inside the corpus

PaperYearCited
Learning from Imbalanced Data200910,209
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning20054,049
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…20112,834
Exploratory Undersampling for Class-Imbalance Learning20082,504
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…20182,226
Editorial20042,081
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance20091,890
CLASSIFICATION OF IMBALANCED DATA: A REVIEW20091,713
An insight into classification with imbalanced data: Empirical results and current trends…20131,602
Cost-sensitive boosting for classification of imbalanced data20071,438

Links

DOI · OpenAlex record

Topics

Imbalanced Data Classification TechniquesComputer Science
Machine Learning and Data ClassificationComputer Science
Data Mining Algorithms and ApplicationsComputer Science

Is this record sound?

complete

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

  • supports3 author record(s) attached.
  • supports26 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:46+00:00.

sha256 bba2969b3567609a…