Who Cited It

C4.5: Programs for Machine Learning

1992 · 23,704 citations · 48 from inside this corpus

J. R. Quinlan

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.

C4.5: Programs for Machine Learning (1992)C4.5: Programs for Machine Le…SMOTE: Synthetic Minority Over-sampling Technique (2002)SMOTE: Synthetic Minority Ove…Extremely randomized trees (2006)Extremely randomized treesWrappers for feature subset selection (1997)Wrappers for feature subset s…Text categorization with Support Vector Machines: Learning with many relevant features (1998)Text categorization with Supp…Experiments with a new boosting algorithm (1996)Experiments with a new boosti…The use of the area under the ROC curve in the evaluation of machine learning algorithms (1997)The use of the area under the…The random subspace method for constructing decision forests (1998)The random subspace method fo…Multitask Learning (1997)Multitask LearningBayesian Network Classifiers (1997)Bayesian Network ClassifiersA study of the behavior of several methods for balancing machine learning training data (2004)A study of the behavior of se…Supervised Machine Learning: A Review of Classification Techniques (2007)Supervised Machine Learning: …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 InductionML-KNN: A lazy learning approach to multi-label learning (2007)ML-KNN: A lazy learning appro…A Survey of Outlier Detection Methodologies (2004)A Survey of Outlier Detection…A few useful things to know about machine learning (2012)A few useful things to know a…Selection of relevant features and examples in machine learning (1997)Selection of relevant feature…On the Optimality of the Simple Bayesian Classifier under Zero-One Loss (1997)On the Optimality of the Simp…Understanding Machine Learning: From Theory To Algorithms (2015)Understanding Machine Learnin…Popular Ensemble Methods: An Empirical Study (1999)Popular Ensemble Methods: An …A Short Introduction to Boosting (1999)A Short Introduction to Boost…A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba… (2011)A Review on Ensembles for the…An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia… (1999)An Empirical Comparison of Vo…Multi-Label Classification (2007)Multi-Label ClassificationExtensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values (1998)Irrelevant Features and the Subset Selection Problem (1994)Irrelevant Features and the S…Logistic regression and artificial neural network classification models: a methodology re… (2002)Logistic regression and artif…Using AUC and accuracy in evaluating learning algorithms (2005)Using AUC and accuracy in eva…Editorial (2004)EditorialThe Boosting Approach to Machine Learning: An Overview (2003)The Boosting Approach to Mach…Natural language processing: an introduction (2011)Natural language processing: …Rotation Forest: A New Classifier Ensemble Method (2006)Rotation Forest: A New Classi…RUSBoost: A Hybrid Approach to Alleviating Class Imbalance (2009)RUSBoost: A Hybrid Approach t…CatBoost for big data: an interdisciplinary review (2020)CatBoost for big data: an int…CLASSIFICATION OF IMBALANCED DATA: A REVIEW (2009)CLASSIFICATION OF IMBALANCED …Machine learning for medical diagnosis: history, state of the art and perspective (2001)Machine learning for medical …
36 of 36 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 cites it, inside the corpus

PaperYearCited
SMOTE: Synthetic Minority Over-sampling Technique200232,402
Extremely randomized trees20069,068
Wrappers for feature subset selection19978,991
Text categorization with Support Vector Machines: Learning with many relevant features19988,044
Experiments with a new boosting algorithm19967,582
The use of the area under the ROC curve in the evaluation of machine learning algorithms19977,328
The random subspace method for constructing decision forests19986,904
Multitask Learning19976,478
Bayesian Network Classifiers19974,754
A study of the behavior of several methods for balancing machine learning training data20044,222
Supervised Machine Learning: A Review of Classification Techniques20074,149
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning20054,049
Fast Effective Rule Induction19953,810
ML-KNN: A lazy learning approach to multi-label learning20073,587
A Survey of Outlier Detection Methodologies20043,382
A few useful things to know about machine learning20123,338
Selection of relevant features and examples in machine learning19973,309
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss19973,099
Understanding Machine Learning: From Theory To Algorithms20153,081
Popular Ensemble Methods: An Empirical Study19992,988
A Short Introduction to Boosting19992,952
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…20112,834
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…19992,656
Multi-Label Classification20072,518
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values19982,509
Irrelevant Features and the Subset Selection Problem19942,388
Logistic regression and artificial neural network classification models: a methodology re…20022,201
Using AUC and accuracy in evaluating learning algorithms20052,163
Editorial20042,081
The Boosting Approach to Machine Learning: An Overview20032,023
Natural language processing: an introduction20112,014
Rotation Forest: A New Classifier Ensemble Method20061,911
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance20091,890
CatBoost for big data: an interdisciplinary review20201,776
CLASSIFICATION OF IMBALANCED DATA: A REVIEW20091,713
Machine learning for medical diagnosis: history, state of the art and perspective20011,691

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Topics

Evolutionary Algorithms and ApplicationsComputer Science

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partial

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  • supports1 author record(s) attached.
  • weakensNo references are recorded despite 23,704 citations. A paper this heavily cited did not cite nothing, so the record is incomplete.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

sha256 7e3d99a592f7f61f…