Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
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An Analytical Study on Automatic Classification of Domestic Journal articles Based on Machine Learning

기계학습에 기초한 국내 학술지 논문의 자동분류에 관한 연구

  • Received : 2018.05.17
  • Accepted : 2018.06.19
  • Published : 2018.06.30
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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.

문헌정보학 분야의 국내 학술지 논문으로 구성된 문헌집합을 대상으로 기계학습에 기초한 자동분류의 성능에 영향을 미치는 요소들을 검토하였다. 특히, "정보관리학회지"에 수록된 논문에 주제 범주를 자동 할당하는 분류 성능 측면에서 용어 가중치부여 기법, 학습집합 크기, 분류 알고리즘, 범주 할당 방법 등 주요 요소들의 특성을 다각적인 실험을 통해 살펴보았다. 결과적으로 분류 환경 및 문헌집합의 특성에 따라 각 요소를 적절하게 적용하는 것이 효과적이며, 보다 단순한 모델의 사용으로 상당히 좋은 수준의 성능을 도출할 수 있었다. 또한, 국내 학술지 논문의 분류는 특정 논문에 하나 이상의 범주를 할당하는 복수-범주 분류(multi-label classification)가 실제 환경에 부합한다고 할 수 있다. 따라서 이러한 환경을 고려하여 단순하고 빠른 분류 알고리즘과 소규모의 학습집합을 사용하는 최적의 분류 모델을 제안하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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