A hierarchical Naïve Bayes model for approximate identity matching

G. Alan Wang, Homa Atabakhsh, Hsinchun Chen

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Organizations often manage identity information for their customers, vendors, and employees. Identity management is critical to various organizational practices ranging from customer relationship management to crime investigation. The task of searching for a specific identity is difficult because disparate identity information may exist due to the issues related to unintentional errors and intentional deception. In this paper we propose a hierarchical Naïve Bayes model that improves existing identity matching techniques in terms of searching effectiveness. Experiments show that our proposed model performs significantly better than the exact-match based matching technique. With 50% training instances labeled, the proposed semi-supervised learning achieves a performance comparable to the fully supervised record comparison algorithm. The semi-supervised learning greatly reduces the efforts of manually labeling training instances without significant performance degradation.

Original languageEnglish (US)
Pages (from-to)413-423
Number of pages11
JournalDecision Support Systems
Volume51
Issue number3
DOIs
StatePublished - Jun 1 2011

Keywords

  • EM algorithm
  • Entity matching
  • Hierarchical Naïve Bayes model
  • Identity management
  • Semi-supervised learning

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Fingerprint Dive into the research topics of 'A hierarchical Naïve Bayes model for approximate identity matching'. Together they form a unique fingerprint.

  • Cite this