Work

On the interpretability of fuzzy knowledge base systems

Year: 2024

Type: article

Abstract: In recent years, fuzzy rule-based systems have been attracting great interest in interpretable and eXplainable Artificial Intelligence as ante-hoc methods. These systems represent knowledge that humans can easily understand, but since they are not interpretable per se , they must remain simple and understandable, and the rule base must have a compactness property. This article presents an algorithm for minimizing the fuzzy rule base, leveraging rough set theory and a greedy strategy. Reducing fuzzy rules simplifies the rule base, facilitating the construction of interpretable inference systems such as decision support and recommendation systems. Validation and comparison of the proposed methodology using both real and benchmark data yield encouraging results.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.2558

Authors: Francesco Camastra, Angelo Ciaramella, Giuseppe Salvi, Salvatore Sposato, Antonino Staiano

Institution: Parthenope University of Naples

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,395

Funders: Università degli Studi di Napoli Federico II, Università degli Studi di Napoli Parthenope