Subset selection using rough numeric dependency

About this item

Title

Subset selection using rough numeric dependency

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

In this paper we describe a novel method for performing feature subset selection for supervised learning tasks based on a refined notion of feature relevance. We define relevance as others see it and outline our refinement of this concept. We then describe how we use this new definition in an algorithm to perform subset selection, and finally, we show some preliminary results of using this approach with two quite different supervised learning schemes.

Format

Research paper

Research format

Working or discussion paper

Date created

1995-04

Creator

Smith, Tony C. / Holmes, Geoffrey

URL

https://hdl.handle.net/10289/1090

Related subjects

computer science / feature subset selection / filter model / relevance / machine learning / numeric dependency

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