ICFICE 2019
Current Result Document : 2 / 2
| 한글제목(Korean Title) |
Improving Text Classification Technique |
| 영문제목(English Title) |
Improving Text Classification Technique |
| 저자(Author) |
Liu Xiao-Wen
Guangxing Wang
Abdur Razzaq Fayjie
Hyun-Chang Lee
Seong-Yoon Shin
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| 원문수록처(Citation) |
VOL 11 NO. 01 PP. 0283 ~ 0284 (2019. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Traditional machine learning-based sentiment analysis methods such as Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently the three classification algorithms and the improved algorithm by were compared based on experimental data.
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| 키워드(Keyword) |
Support Vector Machine
kNN
Improved kNN Classification
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| 파일첨부 |
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