By Topic

Novel methods for subset selection with respect to problem knowledge

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. Pudil ; Inst. of Inf. Theory & Autom., Acad. of Sci., Prague, Czech Republic ; J. Hovovicova

Choosing the best method for feature selection depends on the extent of a-priori knowledge of the problem. We present two basic approaches. One involves computationally effective floating-search methods; the other trades off the requirement for a-priori information for the requirement of sufficient data to represent the distributions involved. We've developed methods for statistical pattern recognition that, based on the user's level of knowledge of a problem, can reduce the problem's dimensionality. We believe that these methods can enrich the methodology of subset selection for other fields of AI. This article provides an overview of our methods and techniques. focusing on the basic principles and their potential use

Published in:

IEEE Intelligent Systems and their Applications  (Volume:13 ,  Issue: 2 )