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A Multi-layer Naïve Bayes Model for Approximate Identity Matching.
- Source :
- Intelligence & Security Informatics (9783540344780); 2006, p479-484, 6p
- Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540344780
- Database :
- Supplemental Index
- Journal :
- Intelligence & Security Informatics (9783540344780)
- Publication Type :
- Book
- Accession number :
- 32914048
- Full Text :
- https://doi.org/10.1007/11760146_44