Who Cited It

Using AUC and accuracy in evaluating learning algorithms

2005 · IEEE Transactions on Knowledge and Data Engineering · 2,163 citations · 3 from inside this corpus

Jin Huang, Charles X. Ling

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.

Using AUC and accuracy in evaluating learning algorithms (2005)Using AUC and accuracy in eva…Statistical Learning Theory (1999)Statistical Learning TheoryC4.5: Programs for Machine Learning (1992)C4.5: Programs for Machine Le…A Tutorial on Support Vector Machines for Pattern Recognition (1998)A Tutorial on Support Vector …A training algorithm for optimal margin classifiers (1992)A training algorithm for opti…UCI Repository of machine learning databases (1998)UCI Repository of machine lea…Signal detection theory and psychophysics (1966)Signal detection theory and p…Text categorization with Support Vector Machines: Learning with many relevant features (1998)Text categorization with Supp…The use of the area under the ROC curve in the evaluation of machine learning algorithms (1997)The use of the area under the…Basic principles of ROC analysis (1978)Basic principles of ROC analy…An Introduction to Support Vector Machines (2000)An Introduction to Support Ve…Learning with Kernels (2001)Learning with KernelsA Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification… (2001)A Simple Generalisation of th…The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i… (2020)The advantages of the Matthew…A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba… (2011)A Review on Ensembles for the…An insight into classification with imbalanced data: Empirical results and current trends… (2013)An insight into classificatio…
15 of 15 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
Statistical Learning Theory199926,957
C4.5: Programs for Machine Learning199223,704
A Tutorial on Support Vector Machines for Pattern Recognition199816,488
A training algorithm for optimal margin classifiers199211,664
UCI Repository of machine learning databases199810,546
Signal detection theory and psychophysics196610,002
Text categorization with Support Vector Machines: Learning with many relevant features19988,044
The use of the area under the ROC curve in the evaluation of machine learning algorithms19977,328
Basic principles of ROC analysis19786,099
An Introduction to Support Vector Machines20004,134
Learning with Kernels20012,541
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…20012,307

What cites it, inside the corpus

PaperYearCited
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…20206,132
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…20112,834
An insight into classification with imbalanced data: Empirical results and current trends…20131,602

Links

DOI · OpenAlex record

Topics

Imbalanced Data Classification TechniquesComputer Science
Rough Sets and Fuzzy LogicComputer Science
Advanced Statistical Process MonitoringDecision Sciences

Is this record sound?

complete

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

  • supports2 author record(s) attached.
  • supports56 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 2026-09-04T03:58:50+00:00.

sha256 86a6d6715ff540a0…