Leveraging one-class SVM and semantic analysis to detect anomalous content

Ozgur Yilmazel, Svetlana Symonenko, Niranjan Balasubramanian, Elizabeth D. Liddy

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

Experiments were conducted to test several hypotheses on methods for improving document classification for the malicious insider threat problem within the Intelligence Community. Bag-of-words (BOW) representations of documents were compared to Natural Language Processing (NLP) based representations in both the typical and one-class classification problems using the Support Vector Machine algorithm. Results show that the NLP features significantly improved classifier performance over the BOW approach both in terms of precision and recall, while using many fewer features. The one-class algorithm using NLP features demonstrated robustness when tested on new domains.

Original languageEnglish (US)
Pages (from-to)381-388
Number of pages8
JournalLecture Notes in Computer Science
Volume3495
StatePublished - Sep 26 2005
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2005 - Atlanta, GA, United States
Duration: May 19 2005May 20 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yilmazel, O., Symonenko, S., Balasubramanian, N., & Liddy, E. D. (2005). Leveraging one-class SVM and semantic analysis to detect anomalous content. Lecture Notes in Computer Science, 3495, 381-388.