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

Fatima Chiroma, Mihaela Cocea, Han Liu

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

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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, naïve 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).

Original languageEnglish
Title of host publication2018 Imperial College Computing Student Workshop, ICCSW 2018
EditorsEva Graversen, Edoardo Pirovano
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Number of pages6
EditionEdoardo Pirovano and Eva Graversen
ISBN (Electronic)9783959770972
Publication statusPublished - 1 Jan 2019
Event7th Imperial College Computing Student Workshop - London, United Kingdom
Duration: 20 Sept 201821 Sept 2018

Publication series

NameOpenAccess Series in Informatics
ISSN (Print)2190-6807


Conference7th Imperial College Computing Student Workshop
Abbreviated titleICCSW 2018
Country/TerritoryUnited Kingdom


  • feature selection
  • rule-based learning
  • wrapper approach


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