Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems

• Published in: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp. 1 - 7
• Main Authors: Gacto, Maria Jose, Galende, Marta, Alcala, Rafael, Herrera, Francisco
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2013
• Subjects: Accuracy; Accurate Fuzzy Modeling; Genetics; High-Dimensional and Large-Scale Problems; Input variables; Kalman filters; Multi-Objective Genetic Algorithms; Regression; Sociology; Statistics; Tuning
• DOI: 10.1109/FUZZ-IEEE.2013.6622381
• ISSN: 1098-7584

This paper addresses the challenging problem of fuzzy modeling in high-dimensional and large scale regression datasets. To this end, we propose a scalable two-stage method for obtaining accurate fuzzy models in high-dimensional regression problems using approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, we propose an effective Multi-Objective Evolutionary Algorithm, based on an embedded genetic Data Base learning (involved variables, granularities and a slight lateral displacement of fuzzy partitions) together with an inductive rule base learning within the same process. The second stage is a post-processing process based on a second MOEA to perform a rule selection and a fine scatter-based tuning of the Membership Functions. Moreover, it incorporates an efficient Kalman filter to estimate the coefficients of the consequent polynomial functions in the Takagi-Sugeno-Kang rules. In both stages, we include mechanisms in order to significantly improve the accuracy of the model and to ensure a fast convergence in high-dimensional regression problems. The proposed method is compared to the classical ANFIS method and to a well-known evolutionary learning algorithm for obtaining accurate TSK systems in 8 datasets with different sizes and dimensions, obtaining better results.