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A new approach for selecting informative features for text classification

  • Zinnar Ghasem
    ,
  • Ingo Frommholz
    ,
  • Carsten Maple
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Open access

Abstract

Selecting useful and informative features to classify text is not only important to decrease the size of the feature space, but as well for the overall performance and precision of machine learning. In this study we propose a new feature selection method called Informative Feature Selector (IFS). Different machine learning algorithms and datasets have been utilised to examine the effectiveness of IFS, and it is compared to well-established methods, namely Information Gain, Odd Ratio, Chi Square, Mutual Information and Class Discriminative Measure. Our experiments show that IFS is able to outperform aforementioned methods and to produce effective and efficient results.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 382-389

Publication milestones

  • Published - 31/12/2015

Publication status

Published - 31/12/2015

Edition

Vol 1458

Volume

1458

Publisher

CEUR-WS, Germany

External Publication IDs

  • handle.net: 10547/624262
  • Scopus: 84944316300

Host publication title

CEUR Workshop Proceedings

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