Classification of confidential documents by using adaptive neuro-fuzzy inference systems

Erdem Alparslan, Adem Karahoca, Hayretdin Bahşi

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

Detecting the security level of a confidential document is a vital task for organizations to protect the confidential information encapsulated in. Diverse classification rules and techniques are being applied by human experts. Increasing number of confidential information in organizations are making difficult to classify all the documents carefully with human effort. A hybrid approach involving support vector classifier and adaptive neuro-fuzzy classifier is proposed in this study. Also states preprocessing tasks required for document classification with natural language processing. To represent term-document relations a recommended metric TF-IDF was chosen to construct a weight matrix. Agglutinative nature of Turkish documents is handled by Turkish stemming algorithms. At the end of the article some experimental results and success metrics are projected with accuracy rates.

Original languageEnglish (US)
Pages (from-to)1412-1417
Number of pages6
JournalProcedia Computer Science
Volume3
DOIs
StatePublished - 2011
Externally publishedYes
Event1st World Conference on Information Technology, WCIT-2010 - Istanbul, Turkey
Duration: Oct 6 2010Oct 10 2010

Keywords

  • ANFIS
  • Document classification
  • Expert systems
  • SVM
  • Turkish NLP

ASJC Scopus subject areas

  • General Computer Science

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