skip to main content
10.1145/1277741.1277927acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
Article

Active learning for class imbalance problem

Published: 23 July 2007 Publication History

Abstract

The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.

References

[1]
A. Bordes, S. Ertekin, J. Weston, and L. Bottou. Fast kernel classifiers with online and active learning. Journal of Machine Learning Research (JMLR), 6:1579--1619, September 2005.
[2]
N. V. Chawla, K. W. Bowyer., L. O. Hall, and W. P. Kegelmeyer. Smote: Synthetic minority over-sampling technique. J. Artificial Intelligence Research, vol. 16, 2002.
[3]
S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research (JMLR), 2:45--66, 2002.

Cited By

View all
  • (2024)Improved Hybrid Bagging Resampling Framework for Deep Learning-Based Side-Channel AnalysisComputers10.3390/computers1308021013:8(210)Online publication date: 20-Aug-2024
  • (2024)Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular DataProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698659(617-625)Online publication date: 14-Nov-2024
  • (2024)Active Learning for Data Quality Control: A SurveyJournal of Data and Information Quality10.1145/366336916:2(1-45)Online publication date: 11-May-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
July 2007
946 pages
ISBN:9781595935977
DOI:10.1145/1277741
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2007

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. active learning
  2. imbalanced data
  3. support vector machines

Qualifiers

  • Article

Conference

SIGIR07
Sponsor:
SIGIR07: The 30th Annual International SIGIR Conference
July 23 - 27, 2007
Amsterdam, The Netherlands

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)42
  • Downloads (Last 6 weeks)7
Reflects downloads up to 06 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Improved Hybrid Bagging Resampling Framework for Deep Learning-Based Side-Channel AnalysisComputers10.3390/computers1308021013:8(210)Online publication date: 20-Aug-2024
  • (2024)Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular DataProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698659(617-625)Online publication date: 14-Nov-2024
  • (2024)Active Learning for Data Quality Control: A SurveyJournal of Data and Information Quality10.1145/366336916:2(1-45)Online publication date: 11-May-2024
  • (2024)A Machine Learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)International Journal of Disaster Risk Reduction10.1016/j.ijdrr.2024.104526(104526)Online publication date: May-2024
  • (2024)Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09931-528:20(11873-11894)Online publication date: 1-Oct-2024
  • (2024)ACTIVE SMOTE for Imbalanced Medical Data ClassificationAdvances in Information Systems, Artificial Intelligence and Knowledge Management10.1007/978-3-031-51664-1_6(81-97)Online publication date: 20-Jan-2024
  • (2023)Cost-Sensitive Online Adaptive Kernel Learning for Large-Scale Imbalanced ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326664835:10(10554-10568)Online publication date: 1-Oct-2023
  • (2023)Travel Mode Choice Prediction Using Imbalanced Machine LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.323768124:4(3795-3808)Online publication date: Apr-2023
  • (2023)A Cognitive-Driven Ordinal Preservation for Multimodal Imbalanced Brain Disease DiagnosisIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.317536015:2(675-689)Online publication date: Jun-2023
  • (2023)LIFEDATA - A Framework for Traceable Active Learning Projects2023 IEEE 31st International Requirements Engineering Conference Workshops (REW)10.1109/REW57809.2023.00088(465-474)Online publication date: Sep-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media