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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Genetic Algorithm Based on Support Vector Machines for Computer Vision Syndrome Classification

Authors : Eva María Artime Ríos, María del Mar Seguí Crespo, Ana Suarez Sánchez, Sergio Luis Suárez Gómez, Fernando Sánchez Lasheras

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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Abstract

The inclusion in workplaces of video display terminals has introduced multiple benefits in the organization of the work. Nevertheless, it also implies a series of risks for the health of the workers, since it can cause ocular and visual disorders, among others.
In this work, a group of eye and vision-related problems associated to prolonged computer use (known as computer vision syndrome) are studied. The aim is to select the characteristics of the subject most relevant for the occurrence of this syndrome, and then, to develop a classification model for its prediction.
The estimation of this problem is made by means of support vector machines for classification. This machine learning technique will be trained with the support of a genetic algorithm. This provides different patterns of parameters to the training of the support vector machine, improving its performance.
The model performance is verified in terms of the area under the ROC curve, which leads to a model with high accuracy in the classification of the syndrome.

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Metadata
Title
Genetic Algorithm Based on Support Vector Machines for Computer Vision Syndrome Classification
Authors
Eva María Artime Ríos
María del Mar Seguí Crespo
Ana Suarez Sánchez
Sergio Luis Suárez Gómez
Fernando Sánchez Lasheras
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_37

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