A study on the importance of selection pressure and low dimensional weak learners to produce robust ensembles

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

Abstract

Ensembles of classifiers have been studied for some time. It is widely known that weak learners should be accurate and diverse. However, in the real world there are many constraints and few have been said about the robustness of ensembles and how to develop it. In the context of ran- dom subspace methods, this paper addresses the question of developing ensembles to face problems under time con- straints. Experiments show that selecting weak learners based on their accuracy can be used to create robust en- sembles. Thus, the selection pressure in ensembles is a key technique to create not just effective ensembles but also robust ones. Moreover, the experiments motivate further research on ensembles made of low dimensional classifiers which achieve general accurate results.

Original languageEnglish
Title of host publicationGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
Pages1755-1756
Number of pages2
DOIs
Publication statusPublished - 2013
Event15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 - Amsterdam, Netherlands
Duration: Jul 6 2013Jul 10 2013

Other

Other15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
CountryNetherlands
CityAmsterdam
Period7/6/137/10/13

All Science Journal Classification (ASJC) codes

  • Computational Mathematics

Fingerprint Dive into the research topics of 'A study on the importance of selection pressure and low dimensional weak learners to produce robust ensembles'. Together they form a unique fingerprint.

Cite this