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

Boomadevi Sekar, Hui Wang

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


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
PublisherWorld Scientific Connecting Great Minds
Number of pages9
ISBN (Print)978-981-3273-22-1
Publication statusPublished - 21 Aug 2018

Publication series

NameBook Series: World Scientific Proceedings Series on Computer Engineering and Information Science
PublisherWorld Scientific Connecting Great Minds

Bibliographical note

The 13th International FLINS Conference 2018 ; Conference date: 21-08-2018 Through 24-08-2018


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