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http://dx.doi.org/10.5392/JKCA.2017.17.01.039

Time-Series based Dataset Selection Method for Effective Text Classification  

Chae, Yeonghun (UST)
Jeong, Do-Heon (KISTI)
Publication Information
The Journal of the Korea Contents Association / v.17, no.1, 2017 , pp. 39-49 More about this Journal
Abstract
As the Internet technology advances, data on the web is increasing sharply. Many research study about incremental learning for classifying effectively in data increasing. Web document contains the time-series data such as published date. If we reflect time-series data to classification, it will be an effective classification. In this study, we analyze the time-series variation of the words. We propose an efficient classification through dividing the dataset based on the analysis of time-series information. For experiment, we corrected 1 million online news articles including time-series information. We divide the dataset and classify the dataset using SVM and $Na{\ddot{i}}ve$ Bayes. In each model, we show that classification performance is increasing. Through this study, we showed that reflecting time-series information can improve the classification performance.
Keywords
SVM; $Na{\ddot{i}}ve$ Bayes; Time-Series Analysis; Machine Learning; Classification;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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