Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification

Authors Fatima Chiroma, Mihaela Cocea, Han Liu

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Fatima Chiroma
  • School of Computing, University of Portsmouth, United Kingdom
Mihaela Cocea
  • School of Computing, University of Portsmouth, United Kingdom
Han Liu
  • School of Computer Science and Informatics, Cardiff University, United Kingdom

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Fatima Chiroma, Mihaela Cocea, and Han Liu. Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 6:1-6:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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).

Subject Classification

ACM Subject Classification
  • Computing methodologies → Feature selection
  • Feature Selection
  • Prism
  • Rule-based Learning
  • Wrapper Approach


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