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Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutio...

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Detalles Bibliográficos
Autores principales: Mala, S., Latha, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4276354/
https://www.ncbi.nlm.nih.gov/pubmed/25574185
http://dx.doi.org/10.1155/2014/713818
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author Mala, S.
Latha, K.
author_facet Mala, S.
Latha, K.
author_sort Mala, S.
collection PubMed
description Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.
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spelling pubmed-42763542015-01-08 Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition Mala, S. Latha, K. Comput Math Methods Med Research Article Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. Hindawi Publishing Corporation 2014 2014-12-09 /pmc/articles/PMC4276354/ /pubmed/25574185 http://dx.doi.org/10.1155/2014/713818 Text en Copyright © 2014 S. Mala and K. Latha. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mala, S.
Latha, K.
Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition
title Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition
title_full Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition
title_fullStr Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition
title_full_unstemmed Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition
title_short Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition
title_sort feature selection in classification of eye movements using electrooculography for activity recognition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4276354/
https://www.ncbi.nlm.nih.gov/pubmed/25574185
http://dx.doi.org/10.1155/2014/713818
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