Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System
<p dir="ltr">The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However, since edRVFL generates features for its hidden layers through random projection, it can po...
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2024
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| _version_ | 1864513541975834624 |
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| author | M. Sajid (3070545) |
| author2 | M. Tanveer (1758181) Ponnuthurai N. Suganthan (17347024) |
| author2_role | author author |
| author_facet | M. Sajid (3070545) M. Tanveer (1758181) Ponnuthurai N. Suganthan (17347024) |
| author_role | author |
| dc.creator.none.fl_str_mv | M. Sajid (3070545) M. Tanveer (1758181) Ponnuthurai N. Suganthan (17347024) |
| dc.date.none.fl_str_mv | 2024-06-10T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tfuzz.2024.3411614 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Ensemble_Deep_Random_Vector_Functional_Link_Neural_Network_Based_on_Fuzzy_Inference_System/29899085 |
| 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 Data management and data science Machine learning Big data broad learning system deep learning ensemble deep random vector functional link (edRVFL) ensemble learning fuzzy broad learning system fuzzy inference system (FIS) random vector functional link (RVFL) network Brain modeling Mathematical models Vectors Training Computational modeling Fuzzy systems Deep learning |
| dc.title.none.fl_str_mv | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However, since edRVFL generates features for its hidden layers through random projection, it can potentially lose intricate features or fail to capture certain nonlinear features in its base models (hidden layers). To enhance the feature learning capabilities of edRVFL, we propose a novel edRVFL based on fuzzy inference system (edRVFL-FIS). The proposed edRVFL-FIS leverages the capabilities of two emerging domains, namely deep learning and ensemble approaches, with the intrinsic IF–THEN properties of FIS and produces rich feature representation to train the ensemble model. Each base model of the proposed edRVFL-FIS encompasses the following two key feature augmentation components: 1) unsupervised fuzzy layer features and 2) supervised defuzzified features. The edRVFL-FIS model incorporates diverse clustering methods (R-means, K-means, fuzzy C-means) to establish fuzzy layer rules, resulting in the following three model variations: 1) edRVFL-FIS-R, 2) edRVFL-FIS-K, and 3) edRVFL-FIS-C with distinct fuzzified features and defuzzified features. Within the framework of edRVFL-FIS, each base model utilizes the original, hidden layer, and defuzzified features to make predictions. Experimental results, statistical tests, discussions, and analyses conducted across UCI and NDC datasets consistently demonstrate the superior performance of all variations of the proposed edRVFL-FIS model over baseline models such as fuzzy broad learning system.</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.2024.3411614" target="_blank">https://dx.doi.org/10.1109/tfuzz.2024.3411614</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_27c9c11c09635bae567ec84c6d8af0b4 |
| identifier_str_mv | 10.1109/tfuzz.2024.3411614 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29899085 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference SystemM. Sajid (3070545)M. Tanveer (1758181)Ponnuthurai N. Suganthan (17347024)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningBig databroad learning systemdeep learningensemble deep random vector functional link (edRVFL)ensemble learningfuzzy broad learning systemfuzzy inference system (FIS)random vector functional link (RVFL) networkBrain modelingMathematical modelsVectorsTrainingComputational modelingFuzzy systemsDeep learning<p dir="ltr">The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However, since edRVFL generates features for its hidden layers through random projection, it can potentially lose intricate features or fail to capture certain nonlinear features in its base models (hidden layers). To enhance the feature learning capabilities of edRVFL, we propose a novel edRVFL based on fuzzy inference system (edRVFL-FIS). The proposed edRVFL-FIS leverages the capabilities of two emerging domains, namely deep learning and ensemble approaches, with the intrinsic IF–THEN properties of FIS and produces rich feature representation to train the ensemble model. Each base model of the proposed edRVFL-FIS encompasses the following two key feature augmentation components: 1) unsupervised fuzzy layer features and 2) supervised defuzzified features. The edRVFL-FIS model incorporates diverse clustering methods (R-means, K-means, fuzzy C-means) to establish fuzzy layer rules, resulting in the following three model variations: 1) edRVFL-FIS-R, 2) edRVFL-FIS-K, and 3) edRVFL-FIS-C with distinct fuzzified features and defuzzified features. Within the framework of edRVFL-FIS, each base model utilizes the original, hidden layer, and defuzzified features to make predictions. Experimental results, statistical tests, discussions, and analyses conducted across UCI and NDC datasets consistently demonstrate the superior performance of all variations of the proposed edRVFL-FIS model over baseline models such as fuzzy broad learning system.</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.2024.3411614" target="_blank">https://dx.doi.org/10.1109/tfuzz.2024.3411614</a></p>2024-06-10T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tfuzz.2024.3411614https://figshare.com/articles/journal_contribution/Ensemble_Deep_Random_Vector_Functional_Link_Neural_Network_Based_on_Fuzzy_Inference_System/29899085CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298990852024-06-10T15:00:00Z |
| spellingShingle | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System M. Sajid (3070545) Information and computing sciences Artificial intelligence Data management and data science Machine learning Big data broad learning system deep learning ensemble deep random vector functional link (edRVFL) ensemble learning fuzzy broad learning system fuzzy inference system (FIS) random vector functional link (RVFL) network Brain modeling Mathematical models Vectors Training Computational modeling Fuzzy systems Deep learning |
| status_str | publishedVersion |
| title | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| title_full | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| title_fullStr | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| title_full_unstemmed | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| title_short | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| title_sort | Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Big data broad learning system deep learning ensemble deep random vector functional link (edRVFL) ensemble learning fuzzy broad learning system fuzzy inference system (FIS) random vector functional link (RVFL) network Brain modeling Mathematical models Vectors Training Computational modeling Fuzzy systems Deep learning |