RT info:eu-repo/semantics/conferenceObject
T1 Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems
A1 Gacto, María José
A1 Galende Hernández, Marta
A1 Alcalá, Rafael
A1 Herrera, Francisco
AB 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.
PB IEEE
SN 978-1-4799-0022-0
YR 2013
FD 2013
LK https://uvadoc.uva.es/handle/10324/74723
UL https://uvadoc.uva.es/handle/10324/74723
LA eng
NO 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Hyderabad, India, 2013, pp. 1-7
NO Producción Científica
DS UVaDOC
RD 04-mar-2025