Text size
  • Small
  • Medium
  • Large
Contrast
  • Standard
  • Blue text on blue
  • High contrast (Yellow text on black)
  • Blue text on beige

    Feature Selection: A Useful Preprocessing Step

    19th Annual BCS-IRSG Colloquium on IR

    Aberdeen, UK. 8th - 9th April 1997

    AUTHORS

    I. Moulinier

    ABSTRACT

    Statistical classification techniques and machine learning methods have been applied to some Information Retrieval (IR) problems: routing, filtering and categorization.

    Most of these methods are usually awkward and sometimes intractable in highly dimensional feature spaces.

    In order to reduce dimensionality, feature selection has been introduced as a pre-processing step.

    In this paper, we assess to what extent feature selection can be used without causing a loss in effectiveness. This problem can be tackled since a couple of recent learners do not require a preprocessing step.

    On a text categorization task, using the Reuters-22,173 collection, we give empirical evidence that feature selection is useful: first, the size of the collection index can be drastically reduced without causing a significant loss in categorization effectiveness.

    Then, we show that feature selection speeds up the time required to automatically build the categorization system.

    PAPER FORMATS

    PDF filePDF Version of this Paper (108kb)