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Design of low complexity human anxiety classification model based on machine learning

Title
Design of low complexity human anxiety classification model based on machine learning
Authors
Hong E.Park H.
Ewha Authors
박형곤
SCOPUS Author ID
박형곤scopus
Issue Date
2017
Journal Title
Transactions of the Korean Institute of Electrical Engineers
ISSN
1975-8359JCR Link
Citation
vol. 66, no. 9, pp. 1402 - 1408
Keywords
Classification modelComplexityHuman anxietyMachine learningSupport vector machine
Publisher
Korean Institute of Electrical Engineers
Indexed
SCOPUS; KCI scopus
Abstract
Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity. Copyright © The Korean Institute of Electrical Engineers.
DOI
10.5370/KIEE.2017.66.9.1402
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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