Change search
ReferencesLink to record
Permanent link

Direct link
Using Genetic Programming to Obtain Implicit Diversity
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2009 (English)Conference paper (Refereed)
Abstract [en]

When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles should be built from base classifiers that are both accurate and diverse. The question of how to balance these two properties in order to maximize ensemble accuracy is, however, far from solved and many different techniques for obtaining ensemble diversity exist. One such technique is bagging, where implicit diversity is introduced by training base classifiers on different subsets of available data instances, thus resulting in less accurate, but diverse base classifiers. In this paper, genetic programming is used as an alternative method to obtain implicit diversity in ensembles by evolving accurate, but different base classifiers in the form of decision trees, thus exploiting the inherent inconsistency of genetic programming. The experiments show that the GP approach outperforms standard bagging of decision trees, obtaining significantly higher ensemble accuracy over 25 UCI datasets. This superior performance stems from base classifiers having both higher average accuracy and more diversity. Implicitly introducing diversity using GP thus works very well, since evolved base classifiers tend to be highly accurate and diverse.

Place, publisher, year, edition, pages
IEEE , 2009.
Keyword [en]
genetic programming, bagging, ensembles, diversity, Machine learning
National Category
Computer and Information Science Computer and Information Science
URN: urn:nbn:se:hb:diva-6273Local ID: 2320/5813ISBN: 978-1-4244-2959-2OAI: diva2:886960
2009 IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, Norge
Available from: 2015-12-22 Created: 2015-12-22

Open Access in DiVA

fulltext(158 kB)7 downloads
File information
File name FULLTEXT01.pdfFile size 158 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Johansson, UlfSönströd, CeciliaLöfström, TuveKönig, Rikard
By organisation
School of Business and IT
Computer and Information ScienceComputer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 7 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 8 hits
ReferencesLink to record
Permanent link

Direct link