A multi-layer Naïve Bayes model for approximate identity matching

G. Alan Wang, Hsinchun Chen, Homa Atabakhsh

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

5 Scopus citations

Abstract

Identity management is critical to various governmental practices ranging from providing citizens services to enforcing homeland security. The task of searching for a specific identity is difficult because multiple identity representations may exist due to issues related to unintentional errors and intentional deception. We propose a Naïve Bayes identity matching model that improves existing techniques in terms of effectiveness. Experiments show that our proposed model performs significantly better than the exact-match based technique and achieves higher precision than the record comparison technique, In addition, our model greatly reduces the efforts of manually labeling training instances by employing a semi-supervised learning approach. This training method outperforms both fully supervised and unsupervised learning. With a training dataset that only contains 30% labeled instances, our model achieves a performance comparable to that of a fully supervised learning.

Original languageEnglish (US)
Title of host publicationIntelligence and Security Informatics - IEEE International Conference on Intelligence and Security Informatics, ISI 2006, Proceedings
PublisherSpringer-Verlag
Pages479-484
Number of pages6
ISBN (Print)3540344780, 9783540344780
StatePublished - Jan 1 2006
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2006 - San Diego, CA, United States
Duration: May 23 2006May 24 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3975 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherIEEE International Conference on Intelligence and Security Informatics, ISI 2006
CountryUnited States
CitySan Diego, CA
Period5/23/065/24/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wang, G. A., Chen, H., & Atabakhsh, H. (2006). A multi-layer Naïve Bayes model for approximate identity matching. In Intelligence and Security Informatics - IEEE International Conference on Intelligence and Security Informatics, ISI 2006, Proceedings (pp. 479-484). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3975 LNCS). Springer-Verlag.