TY - JOUR
T1 - Design of low complexity human anxiety classification model based on machine learning
AU - Hong, Eunjae
AU - Park, Hyunggon
N1 - Publisher Copyright:
Copyright © The Korean Institute of Electrical Engineers.
PY - 2017/9
Y1 - 2017/9
N2 - 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.
AB - 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.
KW - Classification model
KW - Complexity
KW - Human anxiety
KW - Machine learning
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85030541763&partnerID=8YFLogxK
U2 - 10.5370/KIEE.2017.66.9.1402
DO - 10.5370/KIEE.2017.66.9.1402
M3 - Article
AN - SCOPUS:85030541763
VL - 66
SP - 1402
EP - 1408
JO - Transactions of the Korean Institute of Electrical Engineers
JF - Transactions of the Korean Institute of Electrical Engineers
SN - 1975-8359
IS - 9
ER -