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http://dx.doi.org/10.5391/JKIIS.2011.21.6.730

Pattern Classification Model Design and Performance Comparison for Data Mining of Time Series Data  

Lee, Soo-Yong (연세대학교 인문예술대학 교양교직과)
Lee, Kyoung-Joung (연세대학교 보건과학대학 의공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.6, 2011 , pp. 730-736 More about this Journal
Abstract
In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.
Keywords
KOSPI; KOSPI200; KODEX200; Arrhythmia; Ventricular Fibrillation(VF); Ventricular Tachycardia(VT);
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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