Abstract
Case-based reasoning has become a successful technique that uses the previous experience as a problem-solving paradigm. It adapts or reuses the solutions of a similar problem to solve a new one. In a case-based reasoning system, it is important to have a good similarity retrieval algorithm to retrieve the most similar cases to the query case. However, we also note that in a medical domain with increased use of electronic health records, the availability of patient cases and the related attributes have increased. Thus, as a preprocessing step or as part of the retrieval algorithm, it becomes critical to select the most informative features to improve the retrieval efficiency and accuracy in a case-based reasoning system. In this paper, we explore random forest, a popular method in machine learning, for feature selection and weighting in a case-based reasoning system and investigate the case retrieval accuracy.
Original language | English |
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Title of host publication | World Scientific Connecting Great Minds |
Pages | 421 |
Number of pages | 429 |
ISBN (Electronic) | 978-981-3273-24-5 |
DOIs | |
Publication status | Published (in print/issue) - 21 Aug 2018 |
Event | The 13th International FLINS Conference 2018 - Northern Ireland, UK, Belfast, United Kingdom Duration: 21 Aug 2018 → 24 Aug 2018 http://scm.ulster.ac.uk/FLINS2018/ |
Publication series
Name | Book Series: World Scientific Proceedings Series on Computer Engineering and Information Science |
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Publisher | World Scientific Connecting Great Minds |
ISSN (Print) | 1793-7868 |
Conference
Conference | The 13th International FLINS Conference 2018 |
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Country/Territory | United Kingdom |
City | Belfast |
Period | 21/08/18 → 24/08/18 |
Internet address |