Article

Free access

Combining classifiers in text categorization

Authors:

SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 289 - 297

https://doi.org/10.1145/243199.243276

Published: 18 August 1996 Publication History

PDF eReader

References

[1]

J. Allan, L. Ballesteros, J. P. Callan, W. B. Croft, and Z. Lu. Recent experiments with INQUERY. In D. K. Harmon, editor, The Fourth Text REtrieval Conference (TREC-4), Gaithersburg, MD, 1996. NIST special publication. To appear.

Google Scholar

[2]

C. Apte, F. Damerau, and S. M. Weiss. Automated learning of decision rules for text categorization. ACM Transactions on information Systems, 12(3):233-251, July 1994.

Digital Library

Google Scholar

[3]

N. Belkin, C. Cool, W. B. Croft, and J. P. Callan. The effect of multiple query representations on information retrieval system performance. In Proceedings of A CM SIGIR '93, pages 339-346, 1993.

Digital Library

Google Scholar

[4]

C. Buckley and G. Salton. Optimization of relevance feedback weights. In Proceedings of A CM SIGIR '95, pages 351-357, 1995.

Digital Library

Google Scholar

[5]

W. S. Cooper. Some inconsistencies and misnomers in probabilistic information retrieval. In Proceedings of A CM SIGIR'91, pages 57-61, 1991.

Digital Library

Google Scholar

[6]

W. B. Croft, T. j. Lucia, J. Cringean, and P. Willett. Retrieving documents by plausible inference: An experimental study. Information Processing and Management, 25(6):599-614, 1989.

Digital Library

Google Scholar

[7]

R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, New York, 1973.

Digital Library

Google Scholar

[8]

E. A. Fox and J. A. Shaw. Combination of multiple searches. In D. K. Harmon, editor, The Second Text REtrieval Conference (TREC-2), pages 243-252, Gaithersburg, MD, 1994. NIST special publication 500- 215.

Google Scholar

[9]

N. Fuhr. Models for retrieval with probabilistic indexing. Information Processing and Management, 25(1):55-72, 1989.

Digital Library

Google Scholar

[10]

N. Fuhr, S. Hartmann, G. Lustig, M. Schwantner, K. Tzeras, and G. Knorz. AIR/X- a rule-based multistage indexing system for large subject fields, in Proceedings of the RIAO '91, pages 606-623, Barcelona Spain, April 1991.

Google Scholar

[11]

D. K. Harmon, editor. The Third Text REtrieval Conference (TREC-3), Gaithersburg, MD, 1995. NIST special publication 500-225.

Crossref

Google Scholar

[12]

J. Katzer, M. McGill, J. Tessier, W. Frakes, and P. Das- Gupta. A study of the overlap among document representations. Information Technology: Research and Development, 1:261-274, 1982.

Google Scholar

[13]

L. S. Larkey and W. B. Croft. Automatic assignment of ICD9 codes to discharge summaries. Technical Report IR-64, University of Massachusetts Center for Intelligent Information Retrieval, 1995.

Google Scholar

[14]

D. Lewis. An evaluation of phrasal and clustered representations on a text categorization task. In Proceedings of A CM SIGIR '92, pages 37-50, 1992.

Digital Library

Google Scholar

[15]

D. Lewis. Evaluating and optimizing autonomous text classification systems. In Proceedings of A CM SI- GIR'95, pages 246-254, 1995.

Digital Library

Google Scholar

[16]

D. D. Lewis. Representation and Learning in Information Retrieval. PhD thesis, University of Massachusetts, 1992.

Digital Library

Google Scholar

[17]

D. D. Lewis and M. Ringuette. A comparison of two learning algorithms for text categorization. In Third Annual Symposium on Document Analysis and Information Retrieval, pages 81-93, University of Nevada, Las Vegas, 1994.

Google Scholar

[18]

M. Maron. Automatic indexing: An experimental inquiry. Journal of the ACM, 8:404-417, 1961.

Digital Library

Google Scholar

[19]

B. Masand, G. Linoff, and D. Waltz. Classifying news stories using memory based reasoning. In Proceedings of A CM SIGIR '92, pages 59-65, 1992.

Digital Library

Google Scholar

[20]

T. B. Rajashekar and W. B. Croft. Combining automatic and manual index representations in probabilistic retrieval. Journal of the American Society for Information Science, 6(4):272-283, May 1995.

Digital Library

Google Scholar

[21]

S. Robertson, S. Walker, S. Jones, M. Hancock-Beanlieu, and M. Gatford. Okapi at TREC-3. In Harmon {1:1.

Google Scholar

[22]

J. Rocchio. Relevance feedback in information retrieval. In G. Salton, editor, The SMART Retrieval System . Experiments in Automatic Document Processing, chapter 14. Prentice Hall, Englewood Cliffs, 1971.

Google Scholar

[23]

G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, MA, 1989.

Digital Library

Google Scholar

[24]

G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4):288-297, 1990.

Crossref

Google Scholar

[25]

J. A. Shaw and E. A. Fox. Combination of multiple searches, in Harmon {11}.

Google Scholar

[26]

S. Soderland, D. Fisher, J. Aseltine, and W. Lehnert. CRYSTAL: Inducing a conceptual dictionary. In Proceedings of the Fourteenth international Joint Conference on Artificial Intelligence, Montreal, Canada, August 1995.

Digital Library

Google Scholar

[27]

C. Stanfill and D. Waltz. Toward memory-based reasoning. Communications of the ACM, 29(12):1213-1228, Dec. 1986.

Digital Library

Google Scholar

[28]

H. Turtle and W. B. Croft. Evaluation of an inference network-based retrieval model. A CM Transactions on Information Systems, 9(3):187-222, July 1991.

Digital Library

Google Scholar

[29]

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

Digital Library

Google Scholar

[30]

Y. Yang and C. G. Chute. An application of Expert Network to clinical classification and MEDLINE indexing. In Proceedings of the Ezghteenth Annual Symposium on Computer Applications in Medical Care, pages 157-161, 1994.

Google Scholar

Cited By

View all

Peng XTan TFan T(2024)Automatic ICD Coding Based on Bias Removal2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)10.1109/ICCECT60629.2024.10546138(53-57)Online publication date: 26-Apr-2024
https://doi.org/10.1109/ICCECT60629.2024.10546138
Sun YSang LWu DHe SChen YDuan HChen HLu X(2024)Enhanced ICD-10 code assignment of clinical textsArtificial Intelligence in Medicine10.1016/j.artmed.2024.102967156:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102967
Uma KFrancis SSun WMoens M(2024)Towards Explainability in Automated Medical Code Prediction from Clinical RecordsIntelligent Systems and Applications10.1007/978-3-031-47718-8_40(593-637)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-47718-8_40
Show More Cited By

Index Terms

Combining classifiers in text categorization

Recommendations

COMBINING MULTIPLE CLASSIFIERS USING DEMPSTER'S RULE FOR TEXT CATEGORIZATION

In this paper we investigate the combination of four machine learning methods for text categorization using Dempster's rule of combination. These methods include Support Vector Machine (SVM), kNN (Nearest Neighbor), kNN model-based approach (kNNM), and ...
Combining binary classifiers in different dichotomy spaces for text categorization
Abstract
Several supervised machine learning applications are commonly represented as multi-class problems, but it is harder to distinguish several classes rather than just two classes. In contrast to the approaches one-against-all and all-...
Graphical abstract

Display Omitted
Highlights
- Combined Dichotomy Transformations (CoDiT) method is proposed.
- CoDiT is a ...
Softly combining an ensemble of classifiers learned from a single convolutional neural network for scene categorization
Graphical abstract

Display Omitted
Highlights
- An ensemble of classifiers are trained from a single convolutional neural network.
Abstract
In this paper we propose to train an ensemble of classifiers from a single convolutional neural network (CNN) and softly combine these classifiers for scene categorization. Specifically, we explore the hierarchical structure of a CNN ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval

August 1996

344 pages

ISBN:0897917928

DOI:10.1145/243199

Chairmen:
Hans-Peter Frei
Ubilab, Union Bank, Switzerland
,
Donna Harman,
Peter Schaübie,
Ross Wilkinson

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 1996

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

19SIGIR96

Sponsor:

SIGIR

19SIGIR96: 19th International Conference on Research Development in Information Retrieval

August 18 - 22, 1996

Zurich, Switzerland

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

204
Total Citations
View Citations
1,861
Total Downloads

Downloads (Last 12 months)116
Downloads (Last 6 weeks)9

Reflects downloads up to 28 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Peng XTan TFan T(2024)Automatic ICD Coding Based on Bias Removal2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)10.1109/ICCECT60629.2024.10546138(53-57)Online publication date: 26-Apr-2024
https://doi.org/10.1109/ICCECT60629.2024.10546138
Sun YSang LWu DHe SChen YDuan HChen HLu X(2024)Enhanced ICD-10 code assignment of clinical textsArtificial Intelligence in Medicine10.1016/j.artmed.2024.102967156:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102967
Uma KFrancis SSun WMoens M(2024)Towards Explainability in Automated Medical Code Prediction from Clinical RecordsIntelligent Systems and Applications10.1007/978-3-031-47718-8_40(593-637)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-47718-8_40
Deng SWang GWang HChang F(2023)An Artificial-Intelligence-Driven Spanish Poetry Classification FrameworkBig Data and Cognitive Computing10.3390/bdcc70401837:4(183)Online publication date: 14-Dec-2023
https://doi.org/10.3390/bdcc7040183
Liu ZHu YWu XMertes GYang YClifton D(2023)Patient Clustering for Vital Organ Failure Using ICD Code With Graph AttentionIEEE Transactions on Biomedical Engineering10.1109/TBME.2023.324331170:8(2329-2337)Online publication date: Aug-2023
https://doi.org/10.1109/TBME.2023.3243311
Farrelly CSingh YHathaway QCarlsson GChoudhary APaul RDoretto GHimeur YAtalls SMansoor W(2023)Current Topological and Machine Learning Applications for Bias Detection in Text2023 6th International Conference on Signal Processing and Information Security (ICSPIS)10.1109/ICSPIS60075.2023.10343824(190-195)Online publication date: 8-Nov-2023
https://doi.org/10.1109/ICSPIS60075.2023.10343824
Kumar ADas SRoy S(2023)Transfer Learning Improves Unsupervised Assignment of ICD codes with Clinical Notes2023 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH60066.2023.00047(278-287)Online publication date: Jul-2023
https://doi.org/10.1109/ICDH60066.2023.00047
Mou CYe XWu JDai W(2023)Automated ICD Coding Based on Neural Machine Translation2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA56900.2023.10154772(495-500)Online publication date: 26-Apr-2023
https://doi.org/10.1109/ICCCBDA56900.2023.10154772
Yu YQiu TDuan JWang J(2023)Multigranularity Label Prediction Model for Automatic International Classification of Diseases Coding in Clinical TextJournal of Computational Biology10.1089/cmb.2023.009630:8(900-911)Online publication date: 1-Aug-2023
https://doi.org/10.1089/cmb.2023.0096
Li XZhang YLi XPan XWang JLu M(2023)Automatic International Classification of Diseases Coding via Note-Code Interaction Network with Denoising MechanismJournal of Computational Biology10.1089/cmb.2023.007930:8(912-925)Online publication date: 1-Aug-2023
https://doi.org/10.1089/cmb.2023.0079
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

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

Cited By

Index Terms

Recommendations

COMBINING MULTIPLE CLASSIFIERS USING DEMPSTER'S RULE FOR TEXT CATEGORIZATION

Combining binary classifiers in different dichotomy spaces for text categorization

Softly combining an ensemble of classifiers learned from a single convolutional neural network for scene categorization