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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトルGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
ページ1755-1756
ページ数2
DOI
出版ステータス出版済み - 2013
イベント15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 - Amsterdam, オランダ
継続期間: 7 6 20137 10 2013

出版物シリーズ

名前GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion

その他

その他15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
国/地域オランダ
CityAmsterdam
Period7/6/137/10/13

All Science Journal Classification (ASJC) codes

  • 計算数学

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