By Topic

An Overview of Hybrid Soft Computing Techniques for Classifier Design and Feature Selection

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

1 Author(s)
Ashraf Saad ; Dept. of Comput. Sci., Armstrong Atlantic State Univ., Savannah, GA

Rapid developments in computing-related technologies have enabled the collection of large amounts of data at unprecedented rates from diverse systems, both natural and engineered. The availability of such data has motivated the development of intelligent systems to gain new insights into how these systems work, leading thereby to superior decision making. In this paper we present recent advances in using hybrid soft computing techniques to achieve two of the core functionalities needed to build such intelligent systems, namely: feature selection and classifier design. We posit that these two functionalities are coupled and must be solved simultaneously. We give an overview of soft computing techniques, of classification and classifier design, of the concept of feature extraction and feature selection, of hybrid soft computing techniques, and we present approaches for simultaneous feature selection and classifier design using hybrid soft computing techniques. The paper concludes with insights and directions for future work.

Published in:

Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on

Date of Conference:

10-12 Sept. 2008