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- E-ISSN2586-2073
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A Study on automatic assignment of descriptors using machine learning
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Abstract
This study utilizes various approaches of machine learning in the process of automatically assigning descriptors to journal articles. After selecting core journals in the field of information science and organizing test collection from the articles of the past 11 years, the effectiveness of feature selection and the size of training set was examined. In the regard of feature selection, after reducing the feature set by χ2 statistics(CHI) and criteria which prefer high-frequency features(COS, GSS, JAC), the trained Support Vector Machines(SVM) performs the best. With respective to the size of the training set, it significantly influences the performance of Support Vector Machines(SVM) and Voted Perceptron(VTP). but it scarcely affects that of Naive Bayes(NB).
- keywords
- descriptors, automatic indexing, machine learning, feature selection, classifier, text categorization, descriptors, automatic indexing, machine learning, feature selection, classifier, text categorization, 디스크립터, 자동색인, 기계학습, 자질 선정, 분류기, 텍스트 범주화
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