- P-ISSN2466-2542
- KCI
기계학습을 기반으로 한 인터넷 학술문서의 효과적 자동분류에 관한 연구
The Study on the Effective Automatic Classification of Internet Document Using the Machine Learning
초록
본 연구에서는 kNN분류기를 이용한 범주화 방법에 대한 성능 실험을 하였다. kNN분류기와 같은 대부분의 예제기반 자동 분류기법은 학습문서집단의 자질을 축소하게 되는데 자질을 몇 퍼센트 축소함으로써 높은 성능을 얻을 수 있는지를 알아보고자 하였다. 또한, kNN분류기는 학습문서집단에서 검증문서와 가장 유사한 k개의 학습문서를 찾아야 하는데, 이때 가장 적합한 k값은 얼마인지를 실험을 통하여 검증하여 보고자 하였다.
- keywords
- Automatic Text Categorization Techniques, kNN Classifier
Abstract
This study experimented the performance of categorization methods using the kNN classifier. Most sample based automatic text categorization techniques like the kNN classifier reduces the feature set of the training documents. We sought to find out which percentage reductions in the feature set would result in high performances. In addition, the kNN classifier has to find the k number of training documents most similar to the test documents in the training documents. We sought to verify the most appropriate k value through experiments.
- keywords
- Automatic Text Categorization Techniques, kNN Classifier
참고문헌
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