L.A. Belanche, F.F. González. Review and Evaluation of Feature Selection Algorithms in Synthetic Problems


Natural Sciences / Computer Science / Artificial intelligence

Submitted on: Sep 15, 2012, 18:10:17

Description: The main purpose of Feature Subset Selection is to find a reduced subset of attributes from a data set described by a feature set. The task of a feature selection algorithm (FSA) is to provide with a computational solution motivated by a certain definition of relevance or by a reliable evaluation measure. In this paper several fundamental algorithms are studied to assess their performance in a controlled experimental scenario. A measure to evaluate FSAs is devised that computes the degree of matching between the output given by a FSA and the known optimal solutions. An extensive experimental study on synthetic problems is carried out to assess the behaviour of the algorithms in terms of solution accuracy and size as a function of the relevance, irrelevance, redundancy and size of the data samples. The controlled experimental conditions facilitate the derivation of better-supported and meaningful conclusions.

The abstract of this article will be published in the September 2012 issue of "Intellectual Archive Bulletin", ISSN 1929-1329.

The Library and Archives Canada reference page: collectionscanada.gc.ca/ourl/res.php?url_ver=Z39.88......

To read the article posted on Intellectual Archive web site please click the link below.

L_A_Belanche__Review_and_Evaluation.pdf



© 2011-2017 Shiny World Corp. All rights reserved. To reach us please send an e-mail to support@IntellectualArchive.com