Neighborhood Ranking-Based Feature Selection
This article aims to integrate <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-NN regression, false-nearest neighborhood (FNN), and trustworthiness and continuity (T&C) neighborhood-based measures into an efficient and robus...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10422827/ |
| _version_ | 1797319581090447360 |
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| author | Adam Ipkovich Janos Abonyi |
| author_facet | Adam Ipkovich Janos Abonyi |
| author_sort | Adam Ipkovich |
| collection | DOAJ |
| description | This article aims to integrate <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-NN regression, false-nearest neighborhood (FNN), and trustworthiness and continuity (T&C) neighborhood-based measures into an efficient and robust feature selection method to support the identification of nonlinear regression models. The proposed neighborhood ranking-based feature selection technique (NRFS) is validated in three problems, in a linear regression task, in the nonlinear Friedman database, and in the problem of determining the order of nonlinear dynamical models. A neural network is also identified to validate the resulting feature sets. The analysis of the distance correlation also confirms that the method is capable of exploring the nonlinear correlation structure of complex systems. The results illustrate that the proposed NRFS method can select relevant variables for nonlinear regression models. |
| first_indexed | 2024-03-08T04:09:03Z |
| format | Article |
| id | doaj.art-d891918b4e07414c9e6ce6fc7441d4c8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:09:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d891918b4e07414c9e6ce6fc7441d4c82024-02-09T00:01:21ZengIEEEIEEE Access2169-35362024-01-0112201522016810.1109/ACCESS.2024.336267710422827Neighborhood Ranking-Based Feature SelectionAdam Ipkovich0https://orcid.org/0000-0003-0617-1831Janos Abonyi1https://orcid.org/0000-0001-8593-1493HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, HungaryHUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, HungaryThis article aims to integrate <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-NN regression, false-nearest neighborhood (FNN), and trustworthiness and continuity (T&C) neighborhood-based measures into an efficient and robust feature selection method to support the identification of nonlinear regression models. The proposed neighborhood ranking-based feature selection technique (NRFS) is validated in three problems, in a linear regression task, in the nonlinear Friedman database, and in the problem of determining the order of nonlinear dynamical models. A neural network is also identified to validate the resulting feature sets. The analysis of the distance correlation also confirms that the method is capable of exploring the nonlinear correlation structure of complex systems. The results illustrate that the proposed NRFS method can select relevant variables for nonlinear regression models.https://ieeexplore.ieee.org/document/10422827/Machine learningnonlinear regressionfeature selectionk-nearest neighborsmodel-free regressiontrustworthiness and continuity |
| spellingShingle | Adam Ipkovich Janos Abonyi Neighborhood Ranking-Based Feature Selection IEEE Access Machine learning nonlinear regression feature selection k-nearest neighbors model-free regression trustworthiness and continuity |
| title | Neighborhood Ranking-Based Feature Selection |
| title_full | Neighborhood Ranking-Based Feature Selection |
| title_fullStr | Neighborhood Ranking-Based Feature Selection |
| title_full_unstemmed | Neighborhood Ranking-Based Feature Selection |
| title_short | Neighborhood Ranking-Based Feature Selection |
| title_sort | neighborhood ranking based feature selection |
| topic | Machine learning nonlinear regression feature selection k-nearest neighbors model-free regression trustworthiness and continuity |
| url | https://ieeexplore.ieee.org/document/10422827/ |
| work_keys_str_mv | AT adamipkovich neighborhoodrankingbasedfeatureselection AT janosabonyi neighborhoodrankingbasedfeatureselection |