Classification of imbalanced documents by feature selection

Yusuke Adachi, Naoya Onimura, Takanori Yamashita, Sachio Hirokawa

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

We previously worked on category classification problem of reuter's newspaper article using SVM and feature selection. In the study, feature selection by SVM-score [Sakai, Hirokawa, 2012] showed high accuracy. It was also expected to be superior to other standard indicators in case data is imbalanced. This study aimed to show the effectiveness of feature selection by SVM-score in machine learning with imbalanced data. For the reuter's data, F-measure was calculated in the classification experiment of all 13 categories. As a result, feature selection by SVM-score shows high f-measure and precision. In addition, we found feature words of negative example improve the classification performance.

本文言語英語
ホスト出版物のタイトルProceedings of 2017 International Conference on Compute and Data Analysis, ICCDA 2017
出版社Association for Computing Machinery
ページ228-232
ページ数5
Part F130280
ISBN(電子版)9781450352413
DOI
出版ステータス出版済み - 5月 19 2017
イベント2017 International Conference on Compute and Data Analysis, ICCDA 2017 - Lakeland, 米国
継続期間: 5月 19 20175月 23 2017

その他

その他2017 International Conference on Compute and Data Analysis, ICCDA 2017
国/地域米国
CityLakeland
Period5/19/175/23/17

!!!All Science Journal Classification (ASJC) codes

  • 人間とコンピュータの相互作用
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • ソフトウェア

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