Evolutionary Interval Type-2 Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction

محورهای موضوعی : artificial intelligent

Aref Safari ¹ , Rahil Hosseini ²

1 - Department of Computer Engineering, Islamic Azad University, Shahr-e-Qods Branch, Tehran, Iran
2 - Department of Computer Engineering, Islamic Azad University, Shahr-e-Qods Branch, Tehran, Iran

تاریخ دریافت : 1401/04/12 تاریخ پذیرش : 1401/07/20 تاریخ انتشار : 1401/12/10

کلید واژه: Evolutionary Algorithm, Type-2 Fuzzy Logic, Time-Series Prediction,

چکیده مقاله :

This study presents Interval Type-2 Fuzzy Evolutionary models to manage uncertainty in the process of uncertain time-series prediction. This study presents two type-2 fuzzy evolutionary models for rule extraction that were proposed: 1) Evolutionary Interval Type-2 Fuzzy Rule Learning (EIT2FRL), and 1) Evolutionary Interval Type-2 Fuzzy Rule-Set Learning (EIT2FRLS). A ROC curve analysis was applied for performance evaluation, and the results were validated using a 10-fold cross-validation technique. The results reveal that the proposed methods have an AUC of 0.96 for EIT2FRLS and 0.93 for EIT2FRL proposed methods. The results are promising for knowledge extraction in uncertain circumstances, predicting uncertain patterns prediction, and making suitable strategies and optimal decisions.

چکیده انگلیسی:

This study presents Interval Type-2 Fuzzy Evolutionary models to manage uncertainty in the process of uncertain time-series prediction. This study presents two type-2 fuzzy evolutionary models for rule extraction that were proposed: 1) Evolutionary Interval Type-2 Fuzzy Rule Learning (EIT2FRL), and 1) Evolutionary Interval Type-2 Fuzzy Rule-Set Learning (EIT2FRLS). A ROC curve analysis was applied for performance evaluation, and the results were validated using a 10-fold cross-validation technique. The results reveal that the proposed methods have an AUC of 0.96 for EIT2FRLS and 0.93 for EIT2FRL proposed methods. The results are promising for knowledge extraction in uncertain circumstances, predicting uncertain patterns prediction, and making suitable strategies and optimal decisions.