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Improving probabilistic record linkage with a single-layer neural network
Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2017.
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| Format: | Thesis |
| Language: | English English |
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University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering
2019
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| _version_ | 1867613487334686720 |
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| access_status_str | Open Access |
| author | Hamersma, Kris A. |
| author_browse | Hamersma, Kris A. |
| author_facet | Hamersma, Kris A. |
| author_sort | Hamersma, Kris A. |
| collection | Thesis |
| dc_rights_str_mv | © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/68389 |
| institution | University of Pretoria (South Africa) |
| language | English English |
| last_indexed | 2026-06-10T12:36:55.772Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering |
| publisherStr | University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/68389 Improving probabilistic record linkage with a single-layer neural network Hamersma, Kris A. Mini-dissertations (Industrial and Systems Engineering) Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2017. Data analysis requires data to be of a high quality. Unfortunately this is not always the case, especially when data is extracted from di erent data sources. In the case where there is no unique identi er to match data records from multiple data sources alternative methods need to be developed to match the records. Record linkage attempts to do this primarily with deterministic and probabilistic approaches. Deterministic models depend on certain corresponding elds from each record pair to be identical matches to match the record pair together. Probabilistic methods use a set of equations called the Fellegi- Sunter formulae to calculate decision-making weights, which is used to score a record pair on how well they match. If the matching score is above a certain threshold, the record pair is considered to be a match. This project investigates whether the development of a learning algorithm that re nes the weights will improve the probabilistic model's matching accuracy. The dataset that was used to train and test the record linkage models was a set of 92650 record pairs, some of which were matches and some of which were non-matches. It was found that a learning algorithm did improve the matching accuracy of the probabilistic model, although it is likely that the increase in the number of input features will improve the matching performance even more. 2019-02-04T13:10:23Z 2019-02-04T13:10:23Z 2017 2017 Mini Dissertation http://hdl.handle.net/2263/68389 en en © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. PDF application/pdf University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering |
| spellingShingle | Mini-dissertations (Industrial and Systems Engineering) Hamersma, Kris A. Improving probabilistic record linkage with a single-layer neural network |
| title | Improving probabilistic record linkage with a single-layer neural network |
| title_full | Improving probabilistic record linkage with a single-layer neural network |
| title_fullStr | Improving probabilistic record linkage with a single-layer neural network |
| title_full_unstemmed | Improving probabilistic record linkage with a single-layer neural network |
| title_short | Improving probabilistic record linkage with a single-layer neural network |
| title_sort | improving probabilistic record linkage with a single layer neural network |
| topic | Mini-dissertations (Industrial and Systems Engineering) |
| url | http://hdl.handle.net/2263/68389 |
| work_keys_str_mv | AT hamersmakrisa improvingprobabilisticrecordlinkagewithasinglelayerneuralnetwork |