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Active learning approach for document classification applied to financial reports

  • In the contemporary era, large number of companies publish their business performance as a report on the Internet. To make them usable in larger numbers, these reports must be read and labeled. This is inefficient and expensive. This study supports this business need by automatically classifying four different categories of metadata of financial reports. For training of the used random forest classifier, an active and a passive learning strategy are contrasted. The results show a clear advantage of active learning for classifying whether a financial report contains consolidated enterprise data or not. For more complicated multi-class classifications, no advantage is shown so far under the applied active learning strategy, however, there is potential to develop an alternative more efficient strategy.

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Metadaten
Author:Immo Müller
Referee:Roland Müller
Advisor:Diana Hristova
Document Type:Master's Thesis
Language:English
Date of first Publication:2021/06/01
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2021/05/31
Release Date:2022/03/22
Page Number:55
Institutes:FB I - Wirtschaftswissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoUrheberrechtsschutz