Semi-supervised RVFL-based neural networks for solving classification problems
Random Vector Functional Link (RVFL) is widely used on supervised tasks. However, in the real world, we often have a small number of labelled samples and many unlabelled samples. In this paper, we extend RVFLs for semi-supervised tasks based on Manifold Regularization (MR), thus expanding on the...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/154134 |
| _version_ | 1826113096429600768 |
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| author | Yao, Cheng Hui |
| author2 | Ponnuthurai Nagaratnam Suganthan |
| author_facet | Ponnuthurai Nagaratnam Suganthan Yao, Cheng Hui |
| author_sort | Yao, Cheng Hui |
| collection | NTU |
| description | Random Vector Functional Link (RVFL) is widely used on supervised tasks. However, in the
real world, we often have a small number of labelled samples and many unlabelled samples.
In this paper, we extend RVFLs for semi-supervised tasks based on Manifold Regularization
(MR), thus expanding on the application of RVFL to semi-supervised tasks. MR has been
deeply researched in the past decade to improve the quality of classifiers making use of unlabelled
data. Following this MR approach, semi-supervised RVFL (SS-RVFL) demonstrates
great improvements in performance in comparison to typical RVFL networks. The method
enhances RVFL based classifiers for semi-supervised learning while still retaining the efficiency
of RVFL networks.
In these experiments, we are also proposing the use of Deep RVFL networks for semisupervised
learning. There have not been as much research regarding the use of semisupervised
deep RVFL networks. Hence we will be applying the MR approach to Deep RVFL
(dRVFL) and Ensemble Deep RVFL (edRVFL) for semi-supervised classification problems
as well. Deep variants of the RVFL network are able to gain information from various enhanced
patterns which could help in improving the performance of a learning algorithm.
We present an evaluation on well-known datasets to demonstrate the performance of the
proposed methods. |
| first_indexed | 2024-10-01T03:17:56Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/154134 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:17:56Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1541342023-07-07T15:41:05Z Semi-supervised RVFL-based neural networks for solving classification problems Yao, Cheng Hui Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering EPNSugan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Random Vector Functional Link (RVFL) is widely used on supervised tasks. However, in the real world, we often have a small number of labelled samples and many unlabelled samples. In this paper, we extend RVFLs for semi-supervised tasks based on Manifold Regularization (MR), thus expanding on the application of RVFL to semi-supervised tasks. MR has been deeply researched in the past decade to improve the quality of classifiers making use of unlabelled data. Following this MR approach, semi-supervised RVFL (SS-RVFL) demonstrates great improvements in performance in comparison to typical RVFL networks. The method enhances RVFL based classifiers for semi-supervised learning while still retaining the efficiency of RVFL networks. In these experiments, we are also proposing the use of Deep RVFL networks for semisupervised learning. There have not been as much research regarding the use of semisupervised deep RVFL networks. Hence we will be applying the MR approach to Deep RVFL (dRVFL) and Ensemble Deep RVFL (edRVFL) for semi-supervised classification problems as well. Deep variants of the RVFL network are able to gain information from various enhanced patterns which could help in improving the performance of a learning algorithm. We present an evaluation on well-known datasets to demonstrate the performance of the proposed methods. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-12-19T11:58:35Z 2021-12-19T11:58:35Z 2021 Final Year Project (FYP) Yao, C. H. (2021). Semi-supervised RVFL-based neural networks for solving classification problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154134 https://hdl.handle.net/10356/154134 en A1101-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Yao, Cheng Hui Semi-supervised RVFL-based neural networks for solving classification problems |
| title | Semi-supervised RVFL-based neural networks for solving classification problems |
| title_full | Semi-supervised RVFL-based neural networks for solving classification problems |
| title_fullStr | Semi-supervised RVFL-based neural networks for solving classification problems |
| title_full_unstemmed | Semi-supervised RVFL-based neural networks for solving classification problems |
| title_short | Semi-supervised RVFL-based neural networks for solving classification problems |
| title_sort | semi supervised rvfl based neural networks for solving classification problems |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/154134 |
| work_keys_str_mv | AT yaochenghui semisupervisedrvflbasedneuralnetworksforsolvingclassificationproblems |