technical-note

Web-scale classification: web classification in the big data era

Authors:

Georgios PaliourasAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 687 - 688

https://doi.org/10.1145/2556195.2556208

Published: 24 February 2014 Publication History

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Abstract

This paper provides an overview of the workshop Web-Scale Classification: Web Classification in the Big Data Era which was held in New York City, on February 28th as a workshop of the seventh International Conference on Web Search and Data Mining. The goal of the workshop was to discuss and assess recent research focusing on classification and mining in Web-scale category systems. The workshop brought together members of several communities such web mining, machine learning, text classification and social media mining.

Reference

[1]

A. Kosmopoulos, É. Gaussier, G. Paliouras, and S. Aseervatham. The ecir 2010 large scale hierarchical classification workshop. SIGIR Forum, 44(1):23--32, 2010.

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Senoussi MArtieres TVilloutreix P(2024)Partial label learning for automated classification of single-cell transcriptomic profilesPLOS Computational Biology10.1371/journal.pcbi.101200620:4(e1012006)Online publication date: 5-Apr-2024
https://doi.org/10.1371/journal.pcbi.1012006
Qaraei MBabbar R(2023)Meta-classifier free negative sampling for extreme multilabel classificationMachine Learning10.1007/s10994-023-06468-w113:2(675-697)Online publication date: 20-Nov-2023
https://doi.org/10.1007/s10994-023-06468-w
Liang YHsieh CLee T(2023)Fast block-wise partitioning for extreme multi-label classificationData Mining and Knowledge Discovery10.1007/s10618-023-00945-537:6(2192-2215)Online publication date: 26-Jul-2023
https://doi.org/10.1007/s10618-023-00945-5
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Web-scale classification: web classification in the big data era
1. Information systems

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Information

Published In

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2014

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Technical-note

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WSDM 2014

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WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining

February 24 - 28, 2014

New York, New York, USA

Acceptance Rates

WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Senoussi MArtieres TVilloutreix P(2024)Partial label learning for automated classification of single-cell transcriptomic profilesPLOS Computational Biology10.1371/journal.pcbi.101200620:4(e1012006)Online publication date: 5-Apr-2024
https://doi.org/10.1371/journal.pcbi.1012006
Qaraei MBabbar R(2023)Meta-classifier free negative sampling for extreme multilabel classificationMachine Learning10.1007/s10994-023-06468-w113:2(675-697)Online publication date: 20-Nov-2023
https://doi.org/10.1007/s10994-023-06468-w
Liang YHsieh CLee T(2023)Fast block-wise partitioning for extreme multi-label classificationData Mining and Knowledge Discovery10.1007/s10618-023-00945-537:6(2192-2215)Online publication date: 26-Jul-2023
https://doi.org/10.1007/s10618-023-00945-5
Aryal ASharma SKumar Thapa SBhandari SShakya APrasad Panday S(2023)Hierarchical Classification of Disaster News Using Local Classifier per Parent NodeIntelligent Sustainable Systems10.1007/978-981-99-1726-6_19(253-267)Online publication date: 16-Jun-2023
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Dery L(2023)Multi-Label Ranking: Mining Multi-Label and Label Ranking DataMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_23(511-535)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_23
Qaraei MBabbar R(2022)Adversarial examples for extreme multilabel text classificationMachine Learning10.1007/s10994-022-06263-z111:12(4539-4563)Online publication date: 4-Nov-2022
https://doi.org/10.1007/s10994-022-06263-z
Fiok KKarwowski WGutierrez EDavahli MWilamowski MAhram T(2021)Revisiting Text Guide, a Truncation Method for Long Text ClassificationApplied Sciences10.3390/app1118855411:18(8554)Online publication date: 15-Sep-2021
https://doi.org/10.3390/app11188554
Pereira RCosta YSilla C(2021)Handling imbalance in hierarchical classification problems using local classifiers approachesData Mining and Knowledge Discovery10.1007/s10618-021-00762-835:4(1564-1621)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s10618-021-00762-8
Mortier TWydmuch MDembczyński KHüllermeier EWaegeman W(2021)Efficient set-valued prediction in multi-class classificationData Mining and Knowledge Discovery10.1007/s10618-021-00751-xOnline publication date: 6-May-2021
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Stratogiannis GKouris PAlexandridis GSiolas GStamou GStafylopatis A(2021)Semantic enrichment of documents: a classification perspective for ontology-based imbalanced semantic descriptionsKnowledge and Information Systems10.1007/s10115-021-01615-yOnline publication date: 26-Oct-2021
https://doi.org/10.1007/s10115-021-01615-y
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