Optimization of Fuzzy Rule Induction Based on Decision Tree and Truth Table: A Case Study of Multi-Class Fault Diagnosis

Abdelouadoud Kerarmi, Assia Kamal-Idrissi, Amal Seghrouchni, Amal Seghrouchni

2024

Abstract

Fuzzy Logic (FL) offers valuable advantages in multi-classification tasks, offering the capability to deal with imprecise and uncertain data for nuanced decision-making. However, generating precise fuzzy sets requires substantial effort and expertise. Also, the higher the number of rules in the FL system, the longer the model’s computational time is due to the combinatorial complexity. Thus, good data description, knowledge extraction/representation, and rule induction are crucial for developing an FL model. This paper addresses these challenges by proposing an Integrated Truth Table in Decision Tree-based FL model (ITTDTFL) that generates optimized fuzzy sets and rules. C4.5 DT is employed to extract optimized membership functions and rules using Truth Table (TT) by eliminating the redundancy of the rules. The final version of the rules is extracted from the TT and used in the FL model. We compare ITTDTFL with state-of-the-art models, including FU-RIA, RIPPER, and Decision-Tree-based FL. Experiments were conducted on real datasets of machine failure, evaluating the performances based on several factors, including the number of generated rules, accuracy, and computational time. The results demonstrate that the ITTDTFL model achieved the best performance, with an accuracy of 98.92%, less computational time outperforming the other models.

Paper Citation

in Harvard Style

Kerarmi A., Kamal-Idrissi A. and Seghrouchni A. (2024). Optimization of Fuzzy Rule Induction Based on Decision Tree and Truth Table: A Case Study of Multi-Class Fault Diagnosis. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 312-323. DOI: 10.5220/0012378900003636

in Bibtex Style

@conference{icaart24,
author={Abdelouadoud Kerarmi and Assia Kamal-Idrissi and Amal Seghrouchni},
title={Optimization of Fuzzy Rule Induction Based on Decision Tree and Truth Table: A Case Study of Multi-Class Fault Diagnosis},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={312-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012378900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Optimization of Fuzzy Rule Induction Based on Decision Tree and Truth Table: A Case Study of Multi-Class Fault Diagnosis
SN - 978-989-758-680-4
AU - Kerarmi A.
AU - Kamal-Idrissi A.
AU - Seghrouchni A.
PY - 2024
SP - 312
EP - 323
DO - 10.5220/0012378900003636
PB - SciTePress