Generating Interpretable Fuzzy Systems for Classification Problems
This paper presents a new method to generate interpretable fuzzy systems from training data to deal with classification problems. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in another method. Singleton consequents a...
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| Format: | Article |
| Language: | English |
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Instituto Tecnológico Metropolitano
2009-12-01
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| Series: | TecnoLógicas |
| Subjects: | |
| Online Access: | http://itmojs.itm.edu.co/index.php/tecnologicas/article/view/246 |
| _version_ | 1819080948274167808 |
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| author | Juan A. Contreras-Montes Oscar S. Acuña-Camacho |
| author_facet | Juan A. Contreras-Montes Oscar S. Acuña-Camacho |
| author_sort | Juan A. Contreras-Montes |
| collection | DOAJ |
| description | This paper presents a new method to generate interpretable fuzzy systems from training data to deal with classification problems. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in another method. Singleton consequents are generated form the projection of the modal values of each triangular membership function into the output space. Least square method is used to adjust the consequents. The proposed method gets a higher average classification accuracy rate than the existing methods with a reduced number of rules andparameters and without sacrificing the fuzzy system interpretability. The proposed approach is applied to two classical classification problems: Iris data and the Wisconsin Breast Cancer classification problem. |
| first_indexed | 2024-12-21T19:52:59Z |
| format | Article |
| id | doaj.art-a65b84f04d8f434fa91c7172a40e1f53 |
| institution | Directory Open Access Journal |
| issn | 0123-7799 2256-5337 |
| language | English |
| last_indexed | 2024-12-21T19:52:59Z |
| publishDate | 2009-12-01 |
| publisher | Instituto Tecnológico Metropolitano |
| record_format | Article |
| series | TecnoLógicas |
| spelling | doaj.art-a65b84f04d8f434fa91c7172a40e1f532022-12-21T18:52:10ZengInstituto Tecnológico MetropolitanoTecnoLógicas0123-77992256-53372009-12-01023239255219Generating Interpretable Fuzzy Systems for Classification ProblemsJuan A. Contreras-Montes0Oscar S. Acuña-Camacho1PhD in Technical Sciences from CUJAE, La Habana, Cuba. He graduated as an Electric Engineer in 1987 at the Universidad Tecnológica de Bolívar and as specialist in Industrial Automation in 1998 at the same university. He is working for the Department of Naval Engineer at Navy School in CartagenaMSc from UNAB, He graduated as specialist in Industrial Automation from Coruniversitaria, and as Electrical Engineer from UIS. He is working for the Department of Electrial Engineer at Universidad Tecnologica de Bolivar in CartagenaThis paper presents a new method to generate interpretable fuzzy systems from training data to deal with classification problems. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in another method. Singleton consequents are generated form the projection of the modal values of each triangular membership function into the output space. Least square method is used to adjust the consequents. The proposed method gets a higher average classification accuracy rate than the existing methods with a reduced number of rules andparameters and without sacrificing the fuzzy system interpretability. The proposed approach is applied to two classical classification problems: Iris data and the Wisconsin Breast Cancer classification problem.http://itmojs.itm.edu.co/index.php/tecnologicas/article/view/246Sistemas difusosinterpretabilidadproblemas de clasificación. |
| spellingShingle | Juan A. Contreras-Montes Oscar S. Acuña-Camacho Generating Interpretable Fuzzy Systems for Classification Problems TecnoLógicas Sistemas difusos interpretabilidad problemas de clasificación. |
| title | Generating Interpretable Fuzzy Systems for Classification Problems |
| title_full | Generating Interpretable Fuzzy Systems for Classification Problems |
| title_fullStr | Generating Interpretable Fuzzy Systems for Classification Problems |
| title_full_unstemmed | Generating Interpretable Fuzzy Systems for Classification Problems |
| title_short | Generating Interpretable Fuzzy Systems for Classification Problems |
| title_sort | generating interpretable fuzzy systems for classification problems |
| topic | Sistemas difusos interpretabilidad problemas de clasificación. |
| url | http://itmojs.itm.edu.co/index.php/tecnologicas/article/view/246 |
| work_keys_str_mv | AT juanacontrerasmontes generatinginterpretablefuzzysystemsforclassificationproblems AT oscarsacunacamacho generatinginterpretablefuzzysystemsforclassificationproblems |