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http://dx.doi.org/10.17661/jkiiect.2017.10.6.594

Design of Regression Model and Pattern Classifier by Using Principal Component Analysis  

Roh, Seok-Beom (Department of Electrical & Electronic Eng. Joongbu University)
Lee, Dong-Yoon (Department of Electrical & Electronic Eng. Joongbu University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.10, no.6, 2017 , pp. 594-600 More about this Journal
Abstract
The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.
Keywords
Dimension Reduction; Feature Extraction; Pattern Classification; Prediction Model; Principal Component Analysis;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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