Improving document classification using fine-grained weights

Soo Hwan Song, Chang Hwan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper document classification methods using multinomial na¨ıve Bayes are improved in a number of ways. We use the value weighting method, a new fine-grained weighting method, to calculate the weights of the feature values. Our experiments show that the proposed approach outperforms other state-of-the-art methods.

Original languageEnglish
Title of host publicationCurrent Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings
EditorsChang-Hwan Lee, Yongdai Kim, Young Sig Kwon, Juntae Kim, Moonis Ali
PublisherSpringer Verlag
Pages488-492
Number of pages5
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: 10 Jun 201512 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9101
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
Country/TerritoryKorea, Republic of
CitySeoul
Period10/06/1512/06/15

Keywords

  • Multinomial na¨ıve Bayes
  • Value weighting

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