Genetic-Programming-Based Architecture of Fuzzy Modeling: Towards Coping With High-Dimensional Data
| Autor: | Ali Safari Mamaghani, Witold Pedrycz |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Clustering high-dimensional data
Fuzzy rule Computer science business.industry Applied Mathematics Fuzzy set Genetic programming Multitree Fuzzy logic Data modeling Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Artificial intelligence business Cluster analysis |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 29:2774-2784 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2020.3006993 |
| Popis: | This article 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 multitree 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 clustering algorithm. The novelty of this study is two-fold: 1) it comes with the flexibility of the logic-oriented structure of fuzzy models, and 2) 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 fuzzy rule-based models. The article is illustrated through some experiments that provide a detailed insight into the performance of the fuzzy models. A comprehensive comparative analysis is also covered. |
| Databáze: | OpenAIRE |
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