Enhancing the ORCA framework with a new Fuzzy Rule Base System implementation compatible with the JFML library

Áreas de investigación:

• Methodology - Ordinal classification

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

BibTex:

@conference{FuzzyORCA2021, author = "Francisco Javier Rodriguez-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Jose Manuel Soto-Hidalgo and Juan Carlos G{\'a}mez-Granados", 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, {\&}#039;Ordinal Regression and Classification Algorithms framework (ORCA){\&}#039; 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.", booktitle = "Proceedings of the IEEE International Conference on Fuzzy Systems (Fuzz-IEEE2021)", doi = "10.1109/FUZZ45933.2021.9494526", editor = "IEEE", isbn = "978-1-6654-4407-1", issn = "1558-4739", keywords = "ORCA, Fuzzy ORCA, Fuzzy, JFML Library, NSLVOrd", month = "11th-14th July", organization = " Luxembourg, Luxembourg", publisher = "IEEE Press", title = "{E}nhancing the {ORCA} framework with a new {F}uzzy {R}ule {B}ase {S}ystem implementation compatible with the {JFML} library", url = "doi.org/10.1109/FUZZ45933.2021.9494526", year = "2021", }

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.