Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals
Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this pap...
| Main Authors: | , , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21377/1/Feature%20Selection%20using%20Binary%20Simulated%20Kalman%20Filter%20for%20Peak%20Classification1.pdf |
| _version_ | 1796992750958149632 |
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| author | Badaruddin, Muhammad Mohd Falfazli, Mat Jusof Mohd Ibrahim, Shapiai Asrul, Adam Zulkifli, Md. Yusof Kamil Zakwan, Mohd Azmi Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Norrima, Mokhtar |
| author_facet | Badaruddin, Muhammad Mohd Falfazli, Mat Jusof Mohd Ibrahim, Shapiai Asrul, Adam Zulkifli, Md. Yusof Kamil Zakwan, Mohd Azmi Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Norrima, Mokhtar |
| author_sort | Badaruddin, Muhammad |
| collection | UMP |
| description | Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature
selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this paper, another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection
problem. It is found that the BSKF algorithm performed slightly better than the AMSKF algorithm. |
| first_indexed | 2024-03-06T12:24:21Z |
| format | Conference or Workshop Item |
| id | UMPir21377 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:24:21Z |
| publishDate | 2018 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir213772022-06-15T04:02:19Z http://umpir.ump.edu.my/id/eprint/21377/ Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals Badaruddin, Muhammad Mohd Falfazli, Mat Jusof Mohd Ibrahim, Shapiai Asrul, Adam Zulkifli, Md. Yusof Kamil Zakwan, Mohd Azmi Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Norrima, Mokhtar QA75 Electronic computers. Computer science Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this paper, another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection problem. It is found that the BSKF algorithm performed slightly better than the AMSKF algorithm. IEEE 2018 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21377/1/Feature%20Selection%20using%20Binary%20Simulated%20Kalman%20Filter%20for%20Peak%20Classification1.pdf Badaruddin, Muhammad and Mohd Falfazli, Mat Jusof and Mohd Ibrahim, Shapiai and Asrul, Adam and Zulkifli, Md. Yusof and Kamil Zakwan, Mohd Azmi and Nor Hidayati, Abdul Aziz and Zuwairie, Ibrahim and Norrima, Mokhtar (2018) Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals. In: 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS2018) , 8-10 May 2018 , Kuala Lumpur, Malaysia. pp. 1-6.. ISBN 978-1-5386-6539-8 DOI 10.1109/ISMS.2018.00010 |
| spellingShingle | QA75 Electronic computers. Computer science Badaruddin, Muhammad Mohd Falfazli, Mat Jusof Mohd Ibrahim, Shapiai Asrul, Adam Zulkifli, Md. Yusof Kamil Zakwan, Mohd Azmi Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Norrima, Mokhtar Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals |
| title | Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_full | Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_fullStr | Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_full_unstemmed | Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_short | Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals |
| title_sort | feature selection using binary simulated kalman filter for peak classification of eeg signals |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/21377/1/Feature%20Selection%20using%20Binary%20Simulated%20Kalman%20Filter%20for%20Peak%20Classification1.pdf |
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