Title: Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms

Authors: Andreja Naumoski; Georgina Mirceva; Kosta Mitreski

Addresses: Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia ' Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia ' Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

Abstract: The fuzzy-rough rule induction algorithms use fuzzy-rough set concepts such as t-norms, implicators and fuzzy tolerance relationship metrics to calculate the upper and lower approximations. In this direction, the paper examines the influence of the novel Webber t-norm on the model performance obtained with the QuickRules and VQRules algorithms over 19 datasets from different research disciplines. The AUC-ROC metric is used to assess model performance as well as the statistical significance compared to the control model with the highest rank. The obtained results revealed that the k-parameter of the Webber t-norm decreases the model descriptive performance as his value increases, but for the predictive performance of the model there was not any influence by this parameter. In both cases, we were able to identify specific algorithm settings, mostly specific metrics for fuzzy tolerance relations that produce models with high accuracy.

Keywords: Webber t-norm; vaguely quantifiers; fuzzy tolerance relationship metrics; fuzzy rough sets; rule induction algorithms; statistical significance.

DOI: 10.1504/IJDATS.2022.129178

International Journal of Data Analysis Techniques and Strategies, 2022 Vol.14 No.4, pp.302 - 316

Received: 22 Feb 2022
Received in revised form: 23 Sep 2022
Accepted: 24 Dec 2022
Published online: 27 Feb 2023 *

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