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Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series

Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent...

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Main Authors: Qamar, Faizan, Yu, Keping, Al-Khaleefa, Ahmed Salih, Hassan, Rosilah, Ahmad, Mohd Riduan, Wen, Zheng, Mohd Aman, Azana Hafizah
Format: Article
Language:English
Published: Institute of Electronics Information Communication Engineers 2021
Online Access:http://eprints.utem.edu.my/id/eprint/25831/2/E104.D_2020BDP0002.PDF
http://eprints.utem.edu.my/id/eprint/25831/
https://www.jstage.jst.go.jp/article/transinf/E104.D/8/E104.D_2020BDP0002/_pdf/-char/en
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spelling my.utem.eprints.258312022-04-13T10:52:02Z http://eprints.utem.edu.my/id/eprint/25831/ Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series Qamar, Faizan Yu, Keping Al-Khaleefa, Ahmed Salih Hassan, Rosilah Ahmad, Mohd Riduan Wen, Zheng Mohd Aman, Azana Hafizah Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM. Institute of Electronics Information Communication Engineers 2021-08 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25831/2/E104.D_2020BDP0002.PDF Qamar, Faizan and Yu, Keping and Al-Khaleefa, Ahmed Salih and Hassan, Rosilah and Ahmad, Mohd Riduan and Wen, Zheng and Mohd Aman, Azana Hafizah (2021) Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series. IEICE Transactions on Information and Systems, E104-D (8). pp. 1172-1184. ISSN 0916-8532 https://www.jstage.jst.go.jp/article/transinf/E104.D/8/E104.D_2020BDP0002/_pdf/-char/en 10.1587/transinf.2020BDP0002
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM.
format Article
author Qamar, Faizan
Yu, Keping
Al-Khaleefa, Ahmed Salih
Hassan, Rosilah
Ahmad, Mohd Riduan
Wen, Zheng
Mohd Aman, Azana Hafizah
spellingShingle Qamar, Faizan
Yu, Keping
Al-Khaleefa, Ahmed Salih
Hassan, Rosilah
Ahmad, Mohd Riduan
Wen, Zheng
Mohd Aman, Azana Hafizah
Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
author_facet Qamar, Faizan
Yu, Keping
Al-Khaleefa, Ahmed Salih
Hassan, Rosilah
Ahmad, Mohd Riduan
Wen, Zheng
Mohd Aman, Azana Hafizah
author_sort Qamar, Faizan
title Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
title_short Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
title_full Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
title_fullStr Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
title_full_unstemmed Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
title_sort performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series
publisher Institute of Electronics Information Communication Engineers
publishDate 2021
url http://eprints.utem.edu.my/id/eprint/25831/2/E104.D_2020BDP0002.PDF
http://eprints.utem.edu.my/id/eprint/25831/
https://www.jstage.jst.go.jp/article/transinf/E104.D/8/E104.D_2020BDP0002/_pdf/-char/en
_version_ 1731229667998826496
score 13.211869

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