Algorithms for feature selection: An evaluation

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selec...

Full description

Saved in:
Bibliographic Details
Published inProceedings of 13th International Conference on Pattern Recognition Vol. 2; pp. 18 - 22 vol.2
Main Authors Zongker, D., Jain, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Backpropagation algorithms
Computer science
Genetic algorithms
Nonhomogeneous media
Pattern recognition
Stochastic processes
Taxonomy
Testing
Online AccessGet full text

Cover

More Information
Summary:A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. This paper also illustrates the dangers of using feature selection in small sample size situations. It gives the results of applying feature selection to land use classification of SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.
ISBN:9780818672828
081867282X
ISSN:1051-4651
DOI:10.1109/ICPR.1996.546716