Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. T...

Full description

Bibliographic Details
Main Authors:	Al-Khaleefa, Ahmed Salih, Mohd Riduan, Ahmad, Azmi Awang, Md Isa, Mona Riza, Mohd Esa, Al-Saffar, Ahmed Ali Mohammed, Hassan, Mustafa Hamid
Format:	Article
Language:	English
Published:	MDPI 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/24447/ http://umpir.ump.edu.my/id/eprint/24447/ http://umpir.ump.edu.my/id/eprint/24447/ http://umpir.ump.edu.my/id/eprint/24447/1/Feature%20Adaptive%20and%20Cyclic%20Dynamic.pdf

id	ump-24447
recordtype	eprints
spelling	ump-244472019-03-12T01:28:32Z http://umpir.ump.edu.my/id/eprint/24447/ Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Al-Saffar, Ahmed Ali Mohammed Hassan, Mustafa Hamid QA75 Electronic computers. Computer science Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively MDPI 2019 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/24447/1/Feature%20Adaptive%20and%20Cyclic%20Dynamic.pdf Al-Khaleefa, Ahmed Salih and Mohd Riduan, Ahmad and Azmi Awang, Md Isa and Mona Riza, Mohd Esa and Al-Saffar, Ahmed Ali Mohammed and Hassan, Mustafa Hamid (2019) Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine. Applied Sciences, 9 (5). pp. 1-17. ISSN 2076-3417 https://doi.org/10.3390/app9050895 https://doi.org/10.3390/app9050895
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Al-Saffar, Ahmed Ali Mohammed Hassan, Mustafa Hamid Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
description	Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively
format	Article
author	Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Al-Saffar, Ahmed Ali Mohammed Hassan, Mustafa Hamid
author_facet	Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Al-Saffar, Ahmed Ali Mohammed Hassan, Mustafa Hamid
author_sort	Al-Khaleefa, Ahmed Salih
title	Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
title_short	Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
title_full	Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
title_fullStr	Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
title_full_unstemmed	Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
title_sort	feature adaptive and cyclic dynamic learning based on infinite term memory extreme learning machine
publisher	MDPI
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24447/ http://umpir.ump.edu.my/id/eprint/24447/ http://umpir.ump.edu.my/id/eprint/24447/ http://umpir.ump.edu.my/id/eprint/24447/1/Feature%20Adaptive%20and%20Cyclic%20Dynamic.pdf
first_indexed	2023-09-18T22:37:00Z
last_indexed	2023-09-18T22:37:00Z
_version_	1777416662733029376

Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

Similar Items