통계적 전처리 과정을 통한 분류기 성능 향상에 관한 연구Performance Improvement in a Classification Method by using Statistical Pre-processing
- Other Titles
- Performance Improvement in a Classification Method by using Statistical Pre-processing
- Authors
- 한의환; 차형태
- Issue Date
- Jan-2019
- Publisher
- 제어·로봇·시스템학회
- Keywords
- machine learning; emotion recognition; feature selection; EEG
- Citation
- 제어.로봇.시스템학회 논문지, v.25, no.1, pp.69 - 75
- Journal Title
- 제어.로봇.시스템학회 논문지
- Volume
- 25
- Number
- 1
- Start Page
- 69
- End Page
- 75
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/30841
- DOI
- 10.5302/J.ICROS.2019.18.0196
- ISSN
- 1976-5622
- Abstract
- One of the most significant current discussion in AI (Artificial Intelligence) and HCI (Human Computer Interface) is the pattern recognition algorithm. Many methods are available for this purpose, such as, the support vector machine, artificial neural network, and Bayesian decision rule. In these methods, the number of features is the most critical factor affecting the classifier performance. Therefore, we herein propose feature selection and extraction methods to obtain a more effective classifier (higher accuracy and less complexity). To do this, we apply a statistical algorithm. Before we use pattern recognition algorithms, we select features using variance and correlation coefficient. Additionally, we extract the features using the dimension reduction method. We could filter out critical features and reduce the number of features using above process. For an objective evaluation, we use electroencephalogram and the survey data of the DEAP (dataset for emotion analysis using physiological signals). Additionally, we perform a comparison with the existing study. According to the performance evaluation, a classifier with higher accuracy and less computational complexity is obtained.
- Files in This Item
-
Go to Link
- Appears in
Collections - College of Information Technology > ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/30841)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.