More Web Proxy on the site http://driver.im/

Article

Bayesian online classifiers for text classification and filtering

Authors:

Kian Ming Adam Chai,

Hai Leong Chieu,

Hwee Tou NgAuthors Info & Claims

SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 97 - 104

https://doi.org/10.1145/564376.564395

Published: 11 August 2002 Publication History

Abstract

This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.

References

[1]

G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS 2000, volume 13. The MIT Press, 2001.

[2]

D. Cox and E. Snell. Analysis of Binary Data. Chapman & Hall, London, 2nd edition, 1989.

[3]

L. Csató and M. Opper. Sparse representation for Gaussian process models. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS 2000, volume 13. The MIT Press, 2001.

[4]

H. Drucker, B. Shahrary, and D. C. Gibbon. Relevance feedback using support vector machines. In Proceedings of the 2001 International Conference on Machine Learning, 2001.

Digital Library

[5]

T. E. Dunning. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61--74, 1993.

Digital Library

[6]

W. Hersh, C. Buckley, T. Leone, and D. Hickam. OHSUMED: An interactive retrieval evaluation and new large test collection for research. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 192--201, 1994.

Digital Library

[7]

T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (ECML), pages 137--142, 1998.

Digital Library

[8]

T. Joachims. Making large-scale SVM learning practical. In B. Schólkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods --- Support Vector Learning, chapter 11. The MIT Press, 1999.

Digital Library

[9]

D. D. Lewis. Representation and Learning in Information Retrieval. PhD thesis, Department of Computer and Information Science, University of Massachusetts at Amherst, 1992.

Digital Library

[10]

D. D. Lewis. Evaluating and optimizing automomous text classification systems. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 246--254, 1995.

Digital Library

[11]

D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka. Training algorithms for linear text classifiers. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 298--306, 1996.

Digital Library

[12]

D. J. Mackay. Bayesian interpolation. Neural Computation, 4(3):415--447, 1991.

Digital Library

[13]

D. J. Mackay. Information-based objective functions for active data selection. Neural Computation, 4(4):590--604, 1992.

Digital Library

[14]

R. M. Neal. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report CRG-TR-97-2, Department of Computer Science, University of Toronto, January 1997.

[15]

H. T. Ng, W. B. Goh, and K. L. Low. Feature selection, perceptron learning, and a usability case study for text categorization. In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 67--73, 1997.

Digital Library

[16]

M. Opper. Online versus offline learning from random examples: General results. Physical Review Letters, 77:4671--4674, 1996.

[17]

M. Opper. A Bayesian approach to online learning. In D. Saad, editor, On-Line Learning in Neural Networks. Combridge University Press, 1998.

Digital Library

[18]

S. Robertson and D. A. Hull. The TREC-9 filtering track final report. In Proceedings of the 9th Text REtrieval Conference (TREC-9), pages 25--40, 2001.

[19]

G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513--523, 1988.

Digital Library

[20]

S. A. Solla and O. Winther. Optimal perceptron learning: an online Bayesian approach. In D. Saad, editor, On-Line Learning in Neural Networks. Combridge University Press, 1998.

Digital Library

[21]

N. A. Syed, H. Liu, and K. K. Sung. Incremental learning with support vector machines. In Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence (IJCAI-99), 1999.

[22]

C. van Rijsbergen. Information Retrieval. Butterworths, London, 1979.

Digital Library

[23]

V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.

Digital Library

[24]

C. K. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS 2000, volume 13. The MIT Press, 2001.

[25]

O. Winther. Bayesian Mean Field Algorithms for Neural Networks and Gaussian Processes. PhD thesis, University of Copenhagen, CONNECT, The Niels Bohr Institute, 1998.

[26]

Y. Yang. A study on thresholding strategies for text categorization. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 137--145, 2001.

Digital Library

[27]

Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 42--49, 1999.

Digital Library

Cited By

Zeng YChen XJin R(2023)Ensemble Active Learning by Contextual Bandits for AI Incubation in ManufacturingACM Transactions on Intelligent Systems and Technology10.1145/362782115:1(1-26)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3627821
Mesa-Jiménez JStokes LYang QLivina V(2022)MACHINE LEARNING FOR TEXT CLASSIFICATION IN BUILDING MANAGEMENT SYSTEMSJOURNAL OF CIVIL ENGINEERING AND MANAGEMENT10.3846/jcem.2022.1601228:5(408-421)Online publication date: 12-May-2022
https://doi.org/10.3846/jcem.2022.16012
Li KWang AWang LFan HZhang S(2022)Identification of informational and probabilistic independence by adaptive thresholdingIntelligent Data Analysis10.3233/IDA-21594226:5(1139-1160)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-215942
Show More Cited By

Index Terms

Bayesian online classifiers for text classification and filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Bayesian Naïve Bayes classifiers to text classification

Text classification is the task of assigning predefined categories to natural language documents, and it can provide conceptual views of document collections. The Na ve Bayes NB classifier is a family of simple probabilistic classifiers based on a ...
Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values

Each type of classifier has its own advantages as well as certain shortcomings. In this paper, we take the advantages of the associative classifier and the Naive Bayes Classifier to make up the shortcomings of each other, thus improving the accuracy of ...
AdaBoost classifiers for pecan defect classification

Highlights The performance of AdaBoost algorithms were compared with support vector machine and Bayesian classifiers for pecan defect classification. AdaBoost classifiers took least time and gave best classification accuracy. AdaBoost classifiers ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

August 2002

478 pages

ISBN:1581135610

DOI:10.1145/564376

General Chair:
Kalervo Järvelin
University of Tampere, Finland
,
Program Chairs:
Micheline Beaulieu
University of Sheffield, UK
,
Ricardo Baeza-Yates
University of Chile, Chile
,
Sung Hyon Myaeng
Chungnam National University, Korea

Copyright © 2002 ACM.

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

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2002

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGIR02

Sponsor:

SIGIR

SIGIR02: 25th ACM/SIGIR International Conference on Research and Development in Information Retrieval

August 11 - 15, 2002

Tampere, Finland

Acceptance Rates

SIGIR '02 Paper Acceptance Rate 44 of 219 submissions, 20%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
1,845
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zeng YChen XJin R(2023)Ensemble Active Learning by Contextual Bandits for AI Incubation in ManufacturingACM Transactions on Intelligent Systems and Technology10.1145/362782115:1(1-26)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3627821
Mesa-Jiménez JStokes LYang QLivina V(2022)MACHINE LEARNING FOR TEXT CLASSIFICATION IN BUILDING MANAGEMENT SYSTEMSJOURNAL OF CIVIL ENGINEERING AND MANAGEMENT10.3846/jcem.2022.1601228:5(408-421)Online publication date: 12-May-2022
https://doi.org/10.3846/jcem.2022.16012
Li KWang AWang LFan HZhang S(2022)Identification of informational and probabilistic independence by adaptive thresholdingIntelligent Data Analysis10.3233/IDA-21594226:5(1139-1160)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-215942
Kumar MMahto AAfshar Alam MHaq Z(2022)SARS-CoV-2 Prediction of Outbreak and Analysis Using Machine LearningProceedings of Third International Conference on Intelligent Computing, Information and Control Systems10.1007/978-981-16-7330-6_66(899-912)Online publication date: 15-Mar-2022
https://doi.org/10.1007/978-981-16-7330-6_66
(2018)Assessing cyber-incidents using machine learningInternational Journal of Information and Computer Security10.5555/3292793.329279510:4(341-360)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3292793.3292795
Uddin MLee JRizvi SHamada S(2018)Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning ProcessesApplied Sciences10.3390/app80406468:4(646)Online publication date: 20-Apr-2018
https://doi.org/10.3390/app8040646
Chu QLiu SGao XWu FChen GDeng Q(2018)Binoclt: A New Binomial Classification Scheme for Long-Text Mining in Online Social Network2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)10.1109/PCCC.2018.8710994(1-8)Online publication date: Nov-2018
https://doi.org/10.1109/PCCC.2018.8710994
Sakhapara APawade DChapanera HJani HRamgaonkar D(2018)Segregation of Similar and Dissimilar Live RSS News Feeds Based on Similarity MeasuresData Management, Analytics and Innovation10.1007/978-981-13-1274-8_26(333-344)Online publication date: 8-Sep-2018
https://doi.org/10.1007/978-981-13-1274-8_26
Colhon MBădică C(2018)Knowledge-Based Metrics for Document Classification: Online Reviews ExperimentsIntelligent Distributed Computing XII10.1007/978-3-319-99626-4_37(425-435)Online publication date: 15-Sep-2018
https://doi.org/10.1007/978-3-319-99626-4_37
He ZWu JLv P(2017)Multi-label text classification based on the label correlation mixture modelIntelligent Data Analysis10.3233/IDA-16305521:6(1371-1392)Online publication date: 15-Nov-2017
https://doi.org/10.3233/IDA-163055
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents