Efficient classification of binary data stream with concept drifting using conjunction rule based Boolean classifier

Yiou Xiao, Kishan G. Mehrotra, Chilukuri K Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a conjunction rule based classification technique that has good classification performance, is simple, automatically identifies important attributes, and is extremely fast. Due to these properties the classifier is most suitable for “big”/streaming data. Empirical study, using multiple datasets, shows that time complexity, compared with other classifiers, is faster by several factors, especially for large number of attributes without sacrificing performance.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages457-467
Number of pages11
Volume9101
ISBN (Print)9783319190655
DOIs
StatePublished - 2015
Event28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 - Seoul, Korea, Republic of
Duration: Jun 10 2015Jun 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
CountryKorea, Republic of
CitySeoul
Period6/10/156/12/15

Keywords

  • Boolean classifier
  • Concept Drifting
  • Conjunction rule
  • Data stream

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Xiao, Y., Mehrotra, K. G., & Mohan, C. K. (2015). Efficient classification of binary data stream with concept drifting using conjunction rule based Boolean classifier. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9101, pp. 457-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9101). Springer Verlag. https://doi.org/10.1007/978-3-319-19066-2_44