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Article

Collective multi-label classification

Authors:

Nadia Ghamrawi,

Andrew McCallumAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 195 - 200

https://doi.org/10.1145/1099554.1099591

Published: 31 October 2005 Publication History

Abstract

Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multi-label conditional random field (CRF)classification models that directly parameterize label co-occurrences in multi-label classification. Experiments show that the models outperform their single-label counterparts on standard text corpora. Even when multi-labels are sparse, the models improve subset classification error by as much as 40%.

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Cited By

Xu SZhang YAn XPi S(2024)Performance evaluation of seven multi-label classification methods on real-world patent and publication datasetsJournal of Data and Information Science10.2478/jdis-2024-00149:2(81-103)Online publication date: 27-May-2024
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Qu YTran D(2024)A Proposed Weighted Multi-Label Classification Approach for Ancestral Population Identification in Admixed IndividualsProcedia Computer Science10.1016/j.procs.2024.09.520246(1011-1018)Online publication date: 2024
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Han RPeng TWang BLiu LTiwari PWan X(2024)Document-level Relation Extraction with Relation CorrelationsNeural Networks10.1016/j.neunet.2023.11.062171:C(14-24)Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.neunet.2023.11.062
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Collective multi-label classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

cover image ACM Conferences

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Copyright © 2005 ACM.

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Published: 31 October 2005

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CIKM05

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CIKM05: Conference on Information and Knowledge Management

October 31 - November 5, 2005

Bremen, Germany

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Xu SZhang YAn XPi S(2024)Performance evaluation of seven multi-label classification methods on real-world patent and publication datasetsJournal of Data and Information Science10.2478/jdis-2024-00149:2(81-103)Online publication date: 27-May-2024
https://doi.org/10.2478/jdis-2024-0014
Qu YTran D(2024)A Proposed Weighted Multi-Label Classification Approach for Ancestral Population Identification in Admixed IndividualsProcedia Computer Science10.1016/j.procs.2024.09.520246(1011-1018)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.09.520
Han RPeng TWang BLiu LTiwari PWan X(2024)Document-level Relation Extraction with Relation CorrelationsNeural Networks10.1016/j.neunet.2023.11.062171:C(14-24)Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.neunet.2023.11.062
Huang WEngelhard MCoffman MHill EWeng QScheer AMaslow GHenao RDawson GGoldstein B(2024)A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosisJournal of Biomedical Informatics10.1016/j.jbi.2024.104711157(104711)Online publication date: Sep-2024
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Mei YWang XZhang DWang X(2024)Multi-label Out-of-Distribution Detection with Spectral Normalized Joint EnergyWeb and Big Data10.1007/978-981-97-7244-5_3(31-45)Online publication date: 28-Aug-2024
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