Feature selection via the discovery of simple classification rules

About this item

Title

Feature selection via the discovery of simple classification rules

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

It has been our experience that in order to obtain useful results using supervised learning of real-world datasets it is necessary to perform feature subset selection and to perform many experiments using computed aggregates from the most relevant features. It is, therefore, important to look for selection algorithms that work quickly and accurately so that these experiments can be performed in a reasonable length of time, preferably interactively. This paper suggests a method to achieve this...

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Format

Research paper

Research format

Working or discussion paper

Date created

1995-04

Creator

Holmes, Geoffrey / Nevill-Manning, Craig G.

URL

https://hdl.handle.net/10289/1088

Related subjects

computer science / feature subset selection / supervised learning / 1R / filter model / wrapper model / Machine learning

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