Influence of the Yu T-norm on Vaguely Quantified Rough Set Measure Algorithm Accuracy
Journal
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Date Issued
2022-11-16
Author(s)
DOI
10.1109/iceccme55909.2022.9988534
Abstract
This study aims to understand the impact of the Yu T-norm on the Vaguely Quantified Rough Set measurement algorithm, which combines the fuzzy and rough set theories. The algorithm uses both theories and concepts such as lower and higher approximations that integrate numerous features like T-norms, fuzzy tolerance relationship metrics, implicators, ambiguous quantifiers etc. to improve the process of real-world datasets to obtain more accurate models. The investigation process focusses on the experimental evaluation of Yu T-norm models obtained on various real-world datasets. The adjusted p-value is obtained using the insights generated by the AUC-ROC metric from the experimental assessment and a two-step approach for estimating the statistical significance. The results show that the k-parameter in Yu T-norm has impact on model performance and that the five fuzzy tolerance metrics that are studied also have impact on the model's accuracy on unseen data for the Yu T-norm. Therefore, we can conclude that a specific configuration of the k-parameter for the Yu T-norm can directly influence the overfitting of the final model.