Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm

Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm

Peak detection is a significant step in analyzing the electroencephalography (EEG) signal because peaks may represent meaningful brain activities. Several approaches can be used for peak point detection such as time domain, frequency domain, time-frequency domain, and nonlinear approaches. The...

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Main Authors: Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/9084/
http://umpir.ump.edu.my/id/eprint/9084/
http://umpir.ump.edu.my/id/eprint/9084/1/fkee-2014-zuwairie-feature%20selection%20and%20classifier.pdf
id ump-9084
recordtype eprints
spelling ump-90842018-02-08T00:35:04Z http://umpir.ump.edu.my/id/eprint/9084/ Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm Zuwairie, Ibrahim Mohd Zaidi, Mohd Tumari Asrul, Adam Norrima, Mokhtar Marizan, Mubin Mohd Ibrahim, Shapiai TK Electrical engineering. Electronics Nuclear engineering Peak detection is a significant step in analyzing the electroencephalography (EEG) signal because peaks may represent meaningful brain activities. Several approaches can be used for peak point detection such as time domain, frequency domain, time-frequency domain, and nonlinear approaches. The main intention of this study is to find the significant peak features in time domain approach and this can be done using feature selection methods such as gravitational search algorithm (GSA) and particle swarm optimization (PSO). This study focuses on using GSA method, a new computational intelligence algorithm. Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. Using GSA, the parameter estimation of the classifier and the peak feature selection can be done simultaneously. Based on the experimental results, the significant peak features of the peak detection algorithm were obtained where the average test accuracy is 77.74%. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/9084/1/fkee-2014-zuwairie-feature%20selection%20and%20classifier.pdf Zuwairie, Ibrahim and Mohd Zaidi, Mohd Tumari and Asrul, Adam and Norrima, Mokhtar and Marizan, Mubin and Mohd Ibrahim, Shapiai (2014) Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm. In: Proceedings of the 4th International Conference on Artificial Intelligence and Applications in Engineering and Technology (ICAIET 2014), 3-5 December 2014 , Kota Kinabalu, Sabah, Malaysia. pp. 103-108.. https://schttp://www.researchgate.net/profile/Asrul_Adam/publication/269220024_Feature_Selection_and_Classifier_Parameter_Estimation_for_EEG_Signal_Peak_Detection_using_Gravitational_Search_Algorithm/links/548446d00cf25dbd59eb13e8.pdf
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zuwairie, Ibrahim
Mohd Zaidi, Mohd Tumari
Asrul, Adam
Norrima, Mokhtar
Marizan, Mubin
Mohd Ibrahim, Shapiai
Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
description Peak detection is a significant step in analyzing the electroencephalography (EEG) signal because peaks may represent meaningful brain activities. Several approaches can be used for peak point detection such as time domain, frequency domain, time-frequency domain, and nonlinear approaches. The main intention of this study is to find the significant peak features in time domain approach and this can be done using feature selection methods such as gravitational search algorithm (GSA) and particle swarm optimization (PSO). This study focuses on using GSA method, a new computational intelligence algorithm. Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. Using GSA, the parameter estimation of the classifier and the peak feature selection can be done simultaneously. Based on the experimental results, the significant peak features of the peak detection algorithm were obtained where the average test accuracy is 77.74%.
format Conference or Workshop Item
author Zuwairie, Ibrahim
Mohd Zaidi, Mohd Tumari
Asrul, Adam
Norrima, Mokhtar
Marizan, Mubin
Mohd Ibrahim, Shapiai
author_facet Zuwairie, Ibrahim
Mohd Zaidi, Mohd Tumari
Asrul, Adam
Norrima, Mokhtar
Marizan, Mubin
Mohd Ibrahim, Shapiai
author_sort Zuwairie, Ibrahim
title Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
title_short Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
title_full Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
title_fullStr Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
title_full_unstemmed Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
title_sort feature selection and classifier parameter estimation for egg signal peak detection using gravitational search algorithm
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/9084/
http://umpir.ump.edu.my/id/eprint/9084/
http://umpir.ump.edu.my/id/eprint/9084/1/fkee-2014-zuwairie-feature%20selection%20and%20classifier.pdf
first_indexed 2023-09-18T22:07:16Z
last_indexed 2023-09-18T22:07:16Z
_version_ 1777414791831224320

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