Feature Selection and Weighing for Case-based Reasoning System using Random Forests

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationWorld Scientific Connecting Great Minds
Pages421
Number of pages429
ISBN (Electronic)978-981-3273-24-5
DOIs
Publication statusPublished - 21 Aug 2018
EventThe 13th International FLINS Conference 2018 - Northern Ireland, UK, Belfast, United Kingdom
Duration: 21 Aug 201824 Aug 2018
http://scm.ulster.ac.uk/FLINS2018/

Publication series

Name Book Series: World Scientific Proceedings Series on Computer Engineering and Information Science
PublisherWorld Scientific Connecting Great Minds
ISSN (Print)1793-7868

Conference

ConferenceThe 13th International FLINS Conference 2018
CountryUnited Kingdom
CityBelfast
Period21/08/1824/08/18
Internet address

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Cite this

Sekar, B., & Wang, H. (2018). Feature Selection and Weighing for Case-based Reasoning System using Random Forests. In World Scientific Connecting Great Minds (pp. 421). ( Book Series: World Scientific Proceedings Series on Computer Engineering and Information Science). https://doi.org/10.1142/11069, https://doi.org/10.1142/9789813273238_0055