Skip to content

A rule-based classifier with accurate and fast rule term induction for continuous attributes

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

  • Manal Almutairi
  • Frederic Stahl
  • Max Bramer

Rule-based classifiers are considered more expressive, human readable and less prone to over-fitting compared with decision trees, especially when there is noise in the data. Furthermore, rule-based classifiers do not suffer from the replicated subtree problem as classifiers induced by top down induction of decision trees (also known as 'Divide and Conquer'). This research explores some recent developments of a family of rulebased classifiers, the Prism family and more particular G-Prism-FB and G-Prism-DB algorithms, in terms of local discretisation methods used to induce rule terms for continuous data. The paper then proposes a new algorithm of the Prism family based on a combination of Gauss Probability Density Distribution (GPDD), InterQuartile Range (IQR) and data transformation methods. This new rule-based algorithm, termed G-Rules-IQR, is evaluated empirically and outperforms other members of the Prism family in execution time, accuracy and tentative accuracy.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
EditorsM. Arif Wani, Moamar Sayed-Mouchaweh, Edwin Lughofer, Joao Gama, Mehmed Kantardzic
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)978-1-5386-6805-4
ISBN (Print)978-1-5386-6806-1
Publication statusPublished - 17 Jan 2019
Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
Duration: 17 Dec 201820 Dec 2018


Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
CountryUnited States


  • ICMLA2018

    Rights statement: © © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Accepted author manuscript (Post-print), 520 KB, PDF document

Related information

Relations Get citation (various referencing formats)

ID: 13367974