Enhancing the ORCA framework with a new Fuzzy Rule Base System implementation compatible with the JFML library
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- Áreas de investigación:
- Año:
- 2021
- Tipo de publicación:
- Artículo en conferencia
- Palabras clave:
- ORCA, Fuzzy ORCA, Fuzzy, JFML Library, NSLVOrd
- Autores:
-
- Rodriguez-Lozano, Francisco Javier
- Guijo-Rubio, David
- Gutiérrez, Pedro Antonio
- Soto-Hidalgo, Jose Manuel
- Gámez-Granados, Juan Carlos
- Editor:
- IEEE
- Título del libro:
- Proceedings of the IEEE International Conference on Fuzzy Systems (Fuzz-IEEE2021)
- Organización:
- Luxembourg, Luxembourg
- Mes:
- 11th-14th July
- ISBN:
- 978-1-6654-4407-1
- ISSN:
- 1558-4739
- Abstract:
- Classification and regression techniques are two of the main tasks considered by the Machine Learning area. They mainly depend on the target variable to predict. In this context, ordinal classification represents an intermediate task, which is focused on the prediction of nominal variables where the categories follow a specific intrinsic order given by the problem. Nevertheless, the integration of different algorithms able to solve ordinal classification problems is often unavailable in most of existing Machine Learning software, which hinders the use of new approaches. Therefore, this paper focuses on the incorporation of an ordinal classification algorithm (NSLVOrd) in one of the most complete ordinal regression frameworks, 'Ordinal Regression and Classification Algorithms framework (ORCA)' by using both fuzzy rules and the JFML library. The use of NSLVOrd in the ORCA tool as well as a case study with a real database are shown where the obtained results are promising.
