Authors:
Abdelouadoud Kerarmi
1
;
Assia Kamal-Idrissi
1
and
Amal Seghrouchni
1
;
2
Affiliations:
1
Ai Movement - International Artificial Intelligence Center of Morocco - Mohammed VI Polytechnic University, Rabat, Morocco
;
2
Lip6, Sorbonne University, Paris, France
Keyword(s):
Fuzzy Logic, Decision Tree, C4.5 Algorithm, Truth Table, Rule Induction, Knowledge Representation, Multi-Classification, Combinatorial Complexity, Fault Diagnosis.
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-base
d 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.
(More)