Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems
<p dir="ltr">The random vector functional link (RVFL) neural network has shown the potential to overcome traditional artificial neural networks' limitations, such as substantial time consumption and the emergence of suboptimal solutions. However, RVFL struggles to provide compre...
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2024
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| _version_ | 1864513543216300032 |
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| author | M. Sajid (3070545) |
| author2 | A. K. Malik (21793058) M. Tanveer (1758181) Ponnuthurai N. Suganthan (17347024) |
| author2_role | author author author |
| author_facet | M. Sajid (3070545) A. K. Malik (21793058) M. Tanveer (1758181) Ponnuthurai N. Suganthan (17347024) |
| author_role | author |
| dc.creator.none.fl_str_mv | M. Sajid (3070545) A. K. Malik (21793058) M. Tanveer (1758181) Ponnuthurai N. Suganthan (17347024) |
| dc.date.none.fl_str_mv | 2024-05-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tfuzz.2024.3359652 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Neuro-Fuzzy_Random_Vector_Functional_Link_Neural_Network_for_Classification_and_Regression_Problems/29651147 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Broad learning system extreme learning machine fuzzy neural network interpretability neuro-fuzzy random vector functional link (RVFL) network Training Computational modeling Predictive models Adaptation models Optimization Iterative methods |
| dc.title.none.fl_str_mv | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The random vector functional link (RVFL) neural network has shown the potential to overcome traditional artificial neural networks' limitations, such as substantial time consumption and the emergence of suboptimal solutions. However, RVFL struggles to provide comprehensive insights into its decision-making processes. We propose the Neuro-fuzzy RVFL (NF-RVFL) model by combining RVFL with neuro-fuzzy system. The proposed NF-RVFL model takes humanlike decisions based on the IF-THEN approach and enhances its transparency in decision-making. Within this framework, input features undergo a fuzzification process as they traverse the fuzzy layer. The resulting fuzzified features then navigate a hidden layer through random projection as well as yielding defuzzified values via defuzzification. The defuzzified values, hidden layer outputs and original input features collectively contribute to the output prediction process. The proposed NF-RVFL model employs three distinct clustering methods to establish fuzzy layer centers: randomly initialized centers (referred to as R-means), K-means clustering centers, and fuzzy C-means clustering centers. This approach generates three distinct model variations, namely NF-RVFL-R, NF-RVFL-K and NF-RVFL-C, each producing a diverse set of fuzzified and defuzzified samples. Our research involves experiments on various UCI benchmark datasets, covering binary, multiclass classification, and regression tasks. The statistical tests and comprehensive experimental analyses consistently show that all variations of the proposed NF-RVFL model outperform baseline models, highlighting their generalization capabilities. The proposed NF-RVFL models show the generic nature by being adeptly applicable and excelling in regression as well as classification tasks.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Fuzzy Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tfuzz.2024.3359652" target="_blank">https://dx.doi.org/10.1109/tfuzz.2024.3359652</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9a20ca65eed47fd3c208209b2d004853 |
| identifier_str_mv | 10.1109/tfuzz.2024.3359652 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29651147 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression ProblemsM. Sajid (3070545)A. K. Malik (21793058)M. Tanveer (1758181)Ponnuthurai N. Suganthan (17347024)Information and computing sciencesArtificial intelligenceMachine learningBroad learning systemextreme learning machinefuzzy neural networkinterpretabilityneuro-fuzzyrandom vector functional link (RVFL) networkTrainingComputational modelingPredictive modelsAdaptation modelsOptimizationIterative methods<p dir="ltr">The random vector functional link (RVFL) neural network has shown the potential to overcome traditional artificial neural networks' limitations, such as substantial time consumption and the emergence of suboptimal solutions. However, RVFL struggles to provide comprehensive insights into its decision-making processes. We propose the Neuro-fuzzy RVFL (NF-RVFL) model by combining RVFL with neuro-fuzzy system. The proposed NF-RVFL model takes humanlike decisions based on the IF-THEN approach and enhances its transparency in decision-making. Within this framework, input features undergo a fuzzification process as they traverse the fuzzy layer. The resulting fuzzified features then navigate a hidden layer through random projection as well as yielding defuzzified values via defuzzification. The defuzzified values, hidden layer outputs and original input features collectively contribute to the output prediction process. The proposed NF-RVFL model employs three distinct clustering methods to establish fuzzy layer centers: randomly initialized centers (referred to as R-means), K-means clustering centers, and fuzzy C-means clustering centers. This approach generates three distinct model variations, namely NF-RVFL-R, NF-RVFL-K and NF-RVFL-C, each producing a diverse set of fuzzified and defuzzified samples. Our research involves experiments on various UCI benchmark datasets, covering binary, multiclass classification, and regression tasks. The statistical tests and comprehensive experimental analyses consistently show that all variations of the proposed NF-RVFL model outperform baseline models, highlighting their generalization capabilities. The proposed NF-RVFL models show the generic nature by being adeptly applicable and excelling in regression as well as classification tasks.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Fuzzy Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tfuzz.2024.3359652" target="_blank">https://dx.doi.org/10.1109/tfuzz.2024.3359652</a></p>2024-05-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tfuzz.2024.3359652https://figshare.com/articles/journal_contribution/Neuro-Fuzzy_Random_Vector_Functional_Link_Neural_Network_for_Classification_and_Regression_Problems/29651147CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296511472024-05-03T03:00:00Z |
| spellingShingle | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems M. Sajid (3070545) Information and computing sciences Artificial intelligence Machine learning Broad learning system extreme learning machine fuzzy neural network interpretability neuro-fuzzy random vector functional link (RVFL) network Training Computational modeling Predictive models Adaptation models Optimization Iterative methods |
| status_str | publishedVersion |
| title | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| title_full | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| title_fullStr | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| title_full_unstemmed | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| title_short | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| title_sort | Neuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems |
| topic | Information and computing sciences Artificial intelligence Machine learning Broad learning system extreme learning machine fuzzy neural network interpretability neuro-fuzzy random vector functional link (RVFL) network Training Computational modeling Predictive models Adaptation models Optimization Iterative methods |