%0 Report %1 VIE2006-002 %A Zhang, Yang %A Rockett, Peter I. %C UK %D 2006 %K Extraction, Feature MOGP, Multiobjective Optimisation, Pattern Recognition algorithms, genetic programming, %N VIE 2006/001 %T A Generic Optimal Feature Extraction Method using Multiobjective Genetic Programming %U http://www.shef.ac.uk/eee/vie/tech/VIE2006-002.pdf %X In this paper, we present a generic, optimal feature extraction method using multiobjective genetic programming. We reexamine the feature extraction problem and argue that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem.

@techreport{VIE2006-002, abstract = {In this paper, we present a generic, optimal feature extraction method using multiobjective genetic programming. We reexamine the feature extraction problem and argue that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {UK}, author = {Zhang, Yang and Rockett, Peter I.}, biburl = {https://www.bibsonomy.org/bibtex/2581e6ed6d089ad53c33f712bbb66d1a6/brazovayeye}, institution = {Department of Electronic and Electrical Engineering, University of Sheffield}, interhash = {5db72f901e5d55b92d1f6c004b701115}, intrahash = {581e6ed6d089ad53c33f712bbb66d1a6}, keywords = {Extraction, Feature MOGP, Multiobjective Optimisation, Pattern Recognition algorithms, genetic programming,}, number = {VIE 2006/001}, size = {29 pages}, timestamp = {2008-06-19T17:55:48.000+0200}, title = {A Generic Optimal Feature Extraction Method using Multiobjective Genetic Programming}, url = {http://www.shef.ac.uk/eee/vie/tech/VIE2006-002.pdf}, year = 2006 }