DSMM 2016, ACM SIGMOD/PODS International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, July 1, 2016, San Francisco, USA
Financial entities are often referred to with ambiguous descriptions and identifiers. To tackle this issue, the Financial Entity Identification and Information Integration1 (FEIII) Challenge requires participants to automatically reconcile fi- nancial entities among three datasets: the Federal Financial Institution Examination Council2 (FFIEC), the Legal Entity Identifiers (LEI) and the Security and Exchange Commission3 (SEC). Our approach is based on the combination of different Naive Bayes classifiers through an ensemble approach. The evaluation on the Gold Standard developed by the challenge organizers shows F1-scores that are above the average of the other participants for the two proposed tasks.
Type:
Conference
City:
San Francisco
Date:
2016-07-01
Department:
Data Science
Eurecom Ref:
5012
Copyright:
© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in DSMM 2016, ACM SIGMOD/PODS International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, July 1, 2016, San Francisco, USA http://dx.doi.org/10.1145/2951894.2951906
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