- author = "Ali {Safari Mamaghani} and Witold Pedrycz",
- title = "Genetic-Programming based Architecture of Fuzzy Modeling: Towards Coping with High-dimensional Data",
- journal = "IEEE Transactions on Fuzzy Systems",
- year = "2021",
- volume = "29",
- number = "9",
- pages = "2774--2784",
- month = sep,
- keywords = "genetic algorithms, genetic programming, Fuzzy C-means, FCM, gradient descent, high-dimensional data",
- ISSN = "1941-0034",
-
DOI = "
doi:10.1109/TFUZZ.2020.3006993",
- size = "11 pages",
- abstract = "This study is concerned with the development of fuzzy models realized with the aid of genetic programming (GP). The proposed architecture employs GP to form fuzzy logic expressions, involving logic operators and information granules (fuzzy sets) located in the input space, used to predict information granules located in the output space. We propose an architecture realizing logic processing, with the structural optimization of the model accomplished by a multi-tree genetic programming and the parametric optimization completed by gradient-based learning. The granulation of information used in this architecture is developed using the fuzzy C-means (FCM) clustering algorithm. The novelty of this study is two-fold: (i) it comes with the flexibility of the logic-oriented structure of fuzzy models, and (ii) our architecture is designed to handle high-dimensional data by alleviating the detrimental effect of distance concentration hampering the effectiveness of standard Takagi-Sugeno-Kang (TSK) fuzzy rule-based models. The study is illustrated through some experiments that give a detailed insight into the performance of the fuzzy models. A comprehensive comparative analysis is also covered.",
-
notes = "Also known as \cite{9133307}
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2G7, Canada",
