Influence of Algebraic T-norm on Different Indiscernibility Relationships in Fuzzy-Rough Rule Induction Algorithms

The rule induction algorithms generate rules directly in human-understandable if-then form, and this property is essential of successful intelligent classifier. Similar as crisp algorithms, the fuzzy and rough set methods are used to generate rule based induction algorithms. Recently, a rule induction algorithms based on fuzzy-rough theory were proposed. These algorithms operate on the well-known upper and lower approximation concepts, and they are sensitive to different T-norms, implicators and more over; to different similarity metrics. In this paper, we experimentally evaluate the influence of the T-norm Algebraic norm on the classification and regression tasks performance on three fuzzy-rough rule induction algorithms. The experimental results revealed some interesting results, moreover, the choice of similarity metric in combination with the T-norm on some datasets has no influence at all. Based on the experimental results, further investigation is required to investigate the influence of other T-norms on the algorithm’s performance.