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랜덤포레스트를 이용한 국내 학술지 논문의 자동분류에 관한 연구
An Analytical Study on Automatic Classification of Domestic Journal articles Using Random Forest 원문보기

정보관리학회지 = Journal of the Korean society for information management, v.36 no.2 = no.112, 2019년, pp.57 - 77  

김판준 (신라대학교 문헌정보학과)

초록
AI-Helper 아이콘AI-Helper

대표적인 앙상블 기법으로서 랜덤포레스트(RF)를 문헌정보학 분야의 학술지 논문에 대한 자동분류에 적용하였다. 특히, 국내 학술지 논문에 주제 범주를 자동 할당하는 분류 성능 측면에서 트리 수, 자질선정, 학습집합 크기 등 주요 요소들에 대한 다각적인 실험을 수행하였다. 이를 통해, 실제 환경의 불균형 데이터세트(imbalanced dataset)에 대하여 랜덤포레스트(RF)의 성능을 최적화할 수 있는 방안을 모색하였다. 결과적으로 국내 학술지 논문의 자동분류에서 랜덤포레스트(RF)는 트리 수 구간 100~1000(C)과 카이제곱통계량(CHI)으로 선정한 소규모의 자질집합(10%), 대부분의 학습집합(9~10년)을 사용하는 경우에 가장 좋은 분류 성능을 기대할 수 있는 것으로 나타났다.

Abstract ▼ AI-Helper 아이콘AI-Helper

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...

주제어

#자동분류   #자동주석   #디지털 큐레이션   #학술지 논문   #랜덤포레스트   #복수-범주 분류   #불균형 데이터   #자질선정  

표/그림 (11)

참고문헌 (59)

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