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Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui

In the past decade, research related to Human Activity Recognition (HAR) based on devices embedded sensors has shown good overall recognition performance. As a consequence, HAR has been identified as a potential topic for healthcare assessment systems. One of the major research problems is the compu...

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Main Author: Yang , Dong Rui
Format: Thesis
Published: 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://studentsrepo.um.edu.my/12245/1/Yang_Dong_Rui.pdf
http://studentsrepo.um.edu.my/12245/2/Yang_Dong_Rui.pdf
http://studentsrepo.um.edu.my/12245/
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spelling my.um.stud.122452022-01-23T22:56:06Z Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui Yang , Dong Rui QA75 Electronic computers. Computer science In the past decade, research related to Human Activity Recognition (HAR) based on devices embedded sensors has shown good overall recognition performance. As a consequence, HAR has been identified as a potential topic for healthcare assessment systems. One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. Numerous researchers have tried different methods to enhance the algorithm to improve performance, some of these methods include Support Vector Machine (SVM), Decision Trees, Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM), and Deng’s Reduced Kernel Extreme Learning Machine (RKELM). However, unsatisfactory accuracy, slow learning speed, and stability is still a problem. In this study, we have purposed a model named as Optimized Reduced Kernel Extreme Learning Machine (Opt RKELM). It applies the characteristic of ReliefF algorithm to rank and select top scoring features for feature selection. ReliefF can solve the problem of large feature dimension in the existing RKELM. By using clustering method K-Means, we have found the best center point position to calculate Kernel matrix. at last, we have employed Quantum-behaved Particle Swarm Optimization (QPSO) to get the optimal kernel parameter in the proposed model. To evaluate the effectiveness of Opt-RKELM, two benchmark datasets related to human activity recognition problems are used. The notable advantages of the proposed model are excellent recognition accuracy, fast learning speed, stable prediction ability, and good generalization ability. 2019-03 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/12245/1/Yang_Dong_Rui.pdf application/pdf http://studentsrepo.um.edu.my/12245/2/Yang_Dong_Rui.pdf Yang , Dong Rui (2019) Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui. Masters thesis, Universiti Malaya. http://studentsrepo.um.edu.my/12245/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Yang , Dong Rui
Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
description In the past decade, research related to Human Activity Recognition (HAR) based on devices embedded sensors has shown good overall recognition performance. As a consequence, HAR has been identified as a potential topic for healthcare assessment systems. One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. Numerous researchers have tried different methods to enhance the algorithm to improve performance, some of these methods include Support Vector Machine (SVM), Decision Trees, Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM), and Deng’s Reduced Kernel Extreme Learning Machine (RKELM). However, unsatisfactory accuracy, slow learning speed, and stability is still a problem. In this study, we have purposed a model named as Optimized Reduced Kernel Extreme Learning Machine (Opt RKELM). It applies the characteristic of ReliefF algorithm to rank and select top scoring features for feature selection. ReliefF can solve the problem of large feature dimension in the existing RKELM. By using clustering method K-Means, we have found the best center point position to calculate Kernel matrix. at last, we have employed Quantum-behaved Particle Swarm Optimization (QPSO) to get the optimal kernel parameter in the proposed model. To evaluate the effectiveness of Opt-RKELM, two benchmark datasets related to human activity recognition problems are used. The notable advantages of the proposed model are excellent recognition accuracy, fast learning speed, stable prediction ability, and good generalization ability.
format Thesis
author Yang , Dong Rui
author_facet Yang , Dong Rui
author_sort Yang , Dong Rui
title Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
title_short Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
title_full Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
title_fullStr Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
title_full_unstemmed Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
title_sort activity recognition using optimized reduced kernel extreme learning machine (opt-rkelm) / yang dong rui
publishDate 2019
url http://studentsrepo.um.edu.my/12245/1/Yang_Dong_Rui.pdf
http://studentsrepo.um.edu.my/12245/2/Yang_Dong_Rui.pdf
http://studentsrepo.um.edu.my/12245/
_version_ 1738506587255865344
score 13.211869

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