Standard measure and SVM measure for feature selection and their performance effect for text classification

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

8 被引用数 (Scopus)

抄録

This paper compares the prediction performance of document classification based on a variety of feature selection measures. Empirical experiments were conducted for the dataset re0 with 10 measures for feature selection and with SVM. It is confirmed that the feature selection based on the SVM-score proposed by Sakai and Hirokawa (2012) outper-forms the standard measures with small number of features. In fact, 100 words are enough to get the similar performance obtained with all words. The reason of good performance of this feature selection is that the SVM-score capture not only the characteristic words of positive samples but of negative samples as well.

本文言語英語
ホスト出版物のタイトル18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
編集者Maria Indrawan-Santiago, Gabriele Anderst-Kotsis, Matthias Steinbauer, Ismail Khalil
出版社Association for Computing Machinery
ページ262-266
ページ数5
ISBN(電子版)9781450348072
DOI
出版ステータス出版済み - 11月 28 2016
イベント18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Singapore, シンガポール
継続期間: 11月 28 201611月 30 2016

出版物シリーズ

名前ACM International Conference Proceeding Series

その他

その他18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016
国/地域シンガポール
CitySingapore
Period11/28/1611/30/16

!!!All Science Journal Classification (ASJC) codes

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

フィンガープリント

「Standard measure and SVM measure for feature selection and their performance effect for text classification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル