Journal article
CURRENT AND FUTURE TRENDS IN FEATURE SELECTION AND EXTRACTION FOR CLASSIFICATION PROBLEMS
International journal of pattern recognition and artificial intelligence, Vol.19(2), pp.133-142
03/2005
Handle:
https://hdl.handle.net/2376/116728
Abstract
In this article, we describe some of the important currently used methods for solving classification problems, focusing on feature selection and extraction as parts of the overall classification task. We then go on to discuss likely future directions for research in this area, in the context of the other articles from this special issue. We propose that the next major step is the elaboration of a theory of how the methods of selection and extraction interact during the classification process for particular problem domains, along with any learning that may be part of the algorithms. Preferably this theory should be tested on a set of well-established benchmark challenge problems. Using this theory, we will be better able to identify the specific combinations that will achieve best classification performance for new tasks.
Metrics
15 Record Views
Details
- Title
- CURRENT AND FUTURE TRENDS IN FEATURE SELECTION AND EXTRACTION FOR CLASSIFICATION PROBLEMS
- Creators
- LAWRENCE B HOLDER - Department of Computer Science and Engineering, University of Texas at Arlington, TX, USAINGRID RUSSELL - Department of Computer Science, University of Hartford, CT, USAZDRAVKO MARKOV - Department of Computer Science, Central Connecticut State University, CT, USAANTHONY G PIPE - School of Electrical and Computer Engineering, University of West of England, UKBRIAN CARSE - School of Electrical and Computer Engineering, University of West of England, UK
- Publication Details
- International journal of pattern recognition and artificial intelligence, Vol.19(2), pp.133-142
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- World Scientific Publishing Company
- Identifiers
- 99900547464801842
- Language
- English
- Resource Type
- Journal article