A hierarchical Naïve Bayes model for approximate identity matching

22Citations
Citations of this article
58Readers
Mendeley users who have this article in their library.
Get full text

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. © 2011 Elsevier B.V. All rights reserved.

Cite

CITATION STYLE

APA

Wang, G. A., Atabakhsh, H., & Chen, H. (2011). A hierarchical Naïve Bayes model for approximate identity matching. Decision Support Systems, 51(3), 413–423. https://doi.org/10.1016/j.dss.2011.01.007

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free