A general-purpose reduction-intensive feature selector for pattern classification

Feature selection is a critical part of any pattern classification problem. There are many methods for selecting a good set of features. However, for problems where features must be selected from a massive set, most of these methods have accuracy rates that are very low, or computational complexities that are very high. While for some pattern classification problems it might be reasonable to reduce a massive set of features by using application specific information, in problems such as dynamic signature verification this is not possible. Several existing feature selectors are evaluated including the Karhunen-Loeve, SELECT, exhaustive, accelerated, "n best features", sequential forward search, sequential backward search, and the "plus q - take away r" feature selection methods. Each of these methods has particular problems, making them poor candidates for selection of features from a massive set. A General-Purpose Reduction-Intensive (GPRI) feature selector is proposed in this thesis. The GPRI feature selector reduces a large set of features to a small final feature set. The time complexity of the GPRI method is close to the "n best features" method; however, the accuracy rates (obtained with the features selected) far exceeds the "n best features" feature selector. Thus, the GPRI feature selector is a viable candidate for selecting features in general environments where little application specific information is available.

Name thesis:degree_name

M.S.

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Polytechnic Institute and State University

Year dc:date.issued

1986

Author dc:creator

• Klassen, Gregory S.

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10919/106124

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/106124