Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions (Extended Abstract)

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

  • Yuefeng Li
  • Libiao Zhang
  • Yue Xu
  • Yiyu Yao
  • Yutong Wu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIEEE 34th International Conference on Data Engineering, ICDE 2018
PublisherIEEE
Pages1827-1828
ISBN (Electronic)9781538655207
Publication statusPublished - Apr 2018

Publication series

NameInternational Conference on Data Engineering
PublisherIEEE
ISSN (Electronic)2375-026X

Conference

Title34th IEEE International Conference on Data Engineering, ICDE 2018
LocationConservatoire National des Arts et Métiers
PlaceFrance
CityParis
Period16 - 19 April 2018

Abstract

Text classification techniques are playing a crucial role in identifying relevant texts from a large data set, e.g., various online crimes such as Cyberbullying, terrorist recruiting, propaganda or attack planning. Until now, supervised deep learning has brought about breakthroughs in processing multimedia data; however, there was no good practical way to harvest this opportunity for text classification because acquiring and maintaining a massive amount of training examples are too expensive for a large number of categories (e.g., Yahoo! taxonomy contains nearly 300,000 categories and the Library of Congress Subject Headings (LCSH) contains 394,070 subjects). Therefore, the question of how to effectively learn from sparse or small set of training examples is crucial for the true success of text classification.

Research Area(s)

  • Decision rule, Rough set, Text classification, Three way decision, Uncertain decision boundary

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions (Extended Abstract). / Li, Yuefeng; Zhang, Libiao; Xu, Yue et al.
Proceedings: IEEE 34th International Conference on Data Engineering, ICDE 2018. IEEE, 2018. p. 1827-1828 8509492 (International Conference on Data Engineering).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review