Feature selection for human resource selection based on affinity propagation and SVM sensitivity analysis

Qiangwei Wang*, Boyang Li, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

Feature selection is a process to select a subset of original features. It can improve the efficiency and accuracy by removing redundant and irrelevant terms. Feature selection is commonly used in machine learning, and has been wildly applied in many fields. we propose a new feature selection method. This is an integrative hybrid method. It first uses Affinity Propagation and SVM sensitivity analysis to generate feature subset, and then use forward selection and backward elimination method to optimize the feature subset based on feature ranking. Besides, we apply this feature selection method to solve a new problem, Human resource selection. The data is acquired by questionnaire survey. The simulation results show that the proposed feature selection method is effective, it not only reduced human resource features but also increased the classification performance.

本文言語English
ホスト出版物のタイトル2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings
ページ31-36
ページ数6
DOI
出版ステータスPublished - 2009 12月 1
イベント2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Coimbatore, India
継続期間: 2009 12月 92009 12月 11

出版物シリーズ

名前2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings

Conference

Conference2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009
国/地域India
CityCoimbatore
Period09/12/909/12/11

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • ソフトウェア

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