Design of low complexity human anxiety classification model based on machine learning
- 주제(키워드) Classification model , Complexity , Human anxiety , Machine learning , Support vector machine
- 등재 SCOPUS, KCI등재
- 발행기관 Korean Institute of Electrical Engineers
- 발행년도 2017
- URI http://www.dcollection.net/handler/ewha/000000149771
- 본문언어 한국어
- Published As http://dx.doi.org/10.5370/KIEE.2017.66.9.1402
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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.
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