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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
  • Abbr : JKOSIM
  • 2018, 35(2), pp.37~62
  • DOI : 10.3743/KOSIM.2018.35.2.037
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : May 17, 2018
  • Accepted : June 19, 2018
  • Published : June 30, 2018

Kim, Pan Jun 1

1신라대학교

Accredited

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.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.