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Evaluation of rule-based learning and feature selection approaches for classification

Chiroma, Fatima, Cocea, Mihaela and Liu, Han 2019. Evaluation of rule-based learning and feature selection approaches for classification. Presented at: ICCSW18: 2018 Imperial College Computing Student Workshop, London, UK, 20-21 Sep 2018. Proceedings of ICCSW 2018. Schloss Dagstuhl - Leibniz-Zentrum für Informatik,

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Abstract

Feature selection is typically employed before or in conjunction with classification algorithms to reduce the feature dimensionality and improve the classification performance, as well as reduce processing time. While particular approaches have been developed for feature selection, such as filter and wrapper approaches, some algorithms perform feature selection through their learning strategy. In this paper, we are investigating the effect of the implicit feature selection of the PRISM algorithm, which is rule-based, when compared with the wrapper feature selection approach employing four popular algorithms: decision trees, na'ive bayes, k-nearest neighbors and support vector machine. Moreover, we investigate the performance of the algorithms on target classes, i.e. where the aim is to identify one or more phenomena and distinguish them from their absence (i.e. non-target classes), such as when identifying benign and malign cancer (two target classes) vs. non-cancer (the non-target class).

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: In Press
Schools: Computer Science & Informatics
Publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
ISSN: 2190-6807
Related URLs:
Date of First Compliant Deposit: 24 October 2018
Date of Acceptance: 3 August 2018
Last Modified: 02 May 2019 14:53
URI: http://orca.cf.ac.uk/id/eprint/114932

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