Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31
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pISSN : 1013-0799 / eISSN : 2586-2073
- https://journal.kci.go.kr/kosim
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An Analytical Study on Automatic Classification of Domestic Journal articles Using Random Forest
- Journal of the Korean Society for Information Management
- Abbr : JKOSIM
- 2019, 36(2), pp.57~77
- DOI : 10.3743/KOSIM.2019.36.2.057
- Publisher : 한국정보관리학회
- Research Area : Interdisciplinary Studies > Library and Information Science
- Received : May 15, 2019
- Accepted : June 21, 2019
- Published : June 30, 2019
1신라대학교
Accredited
ABSTRACT
Random Forest (RF), a representative ensemble technique, was applied to automatic classification of journal articles in the field of library and information science. Especially, I performed various experiments on the main factors such as tree number, feature selection, and learning set size in terms of classification performance that automatically assigns class labels to domestic journals. Through this, I explored ways to optimize the performance of random forests (RF) for imbalanced datasets in real environments. Consequently, for the automatic classification of domestic journal articles, Random Forest (RF) can be expected to have the best classification performance when using tree number interval 100〜1000(C), small feature set (10%) based on chi-square statistic (CHI), and most learning sets (9-10 years).
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