Evolutionary based feature extraction with dynamic mutation

Eun Yeong Ahn, Tracy Mullen, John Yen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Determining a good feature set is critical to the performance of learning algorithms such as classifiers. Recently, researchers have proposed evolutionary-based feature extraction methods that aim to find a good feature set by combining the original features with new features generated by mathematical transformations of the original features. In this paper, we propose dynamically collecting past performance information on promising features and operators to use in our mutation method. We consider how to make our evolutionary algorithm more efficient and reliable by reducing overfitting. Preliminary results using UCI data show that our dynamic mutation method only slightly enhances the classification accuracy but it produces more reliable results.

Original languageEnglish (US)
Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
Pages409-416
Number of pages8
DOIs
StatePublished - Aug 29 2011
Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States
Duration: Jun 5 2011Jun 8 2011

Publication series

Name2011 IEEE Congress of Evolutionary Computation, CEC 2011

Other

Other2011 IEEE Congress of Evolutionary Computation, CEC 2011
CountryUnited States
CityNew Orleans, LA
Period6/5/116/8/11

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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