인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
초록·키워드
Preprocessing and feature selection are important steps in the machine learning pipeline for text classification. These steps ensure that the input data are appropriately prepared for model training and that the most informative features are selected for the model to learn from. Morpheme analyzer is also crucial in machine learning performance, particularly in text classification tasks that involve languages with complex morphology. This study compares the performance of several feature selection methods, Korean morpheme analyzers, and classifiers in small-scale Korean text classification. PROMETHEE, a multi-criteria decision-making method (MCDM), is used to rank each method based on its performance. As per the results, the document frequency method exhibited the best overall performance among several feature selection methods. In terms of Korean morpheme analyzers, the most recently developed Khaiii demonstrated the best performance. The support vector machine classifier also exhibits suitable performance, suggesting that it is a reliable choice for small-scale Korean text classification. While performance differences among the methods were not significant for each dataset, the overall trend was clear, the aforementioned 3 methods consistently outperformed the others. Through this study, identification of effective techniques for small-scale Korean text classification is feasible, and it is expected to provide guidelines to analysists.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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