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Vol.35 No.2
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An Analytical Study on Automatic Classification of Domestic Journal articles Based on Machine Learning

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2018, v.35 no.2, pp.37-62
https://doi.org/10.3743/KOSIM.2018.35.2.037

(2018). An Analytical Study on Automatic Classification of Domestic Journal articles Based on Machine Learning. Journal of the Korean Society for Information Management, 35(2), 37-62, https://doi.org/10.3743/KOSIM.2018.35.2.037
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Abstract

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in 「Journal of the Korean Society for Information Management」, I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

keywords
automatic classification, text categorization, performance factors, Journal articles, Rocchio, SVM (Support Vector Machine), NB (Naïve Bayes), single-label classification, multi-label classification, machine learning, 자동분류, 텍스트 범주화, 성능 요소, 학술지 논문, 로치오, 지지벡터기계, 나이브 베이즈, 단일-범주 분류, 복수-범주 분류, 기계학습

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