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.
| 元の言語 | 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 | |
| 出版物ステータス | 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 2013 → 7 10 2013 |
その他
| その他 | 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 |
|---|---|
| 国 | オランダ |
| 市 | Amsterdam |
| 期間 | 7/6/13 → 7/10/13 |
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All Science Journal Classification (ASJC) codes
- Computational Mathematics
これを引用
A study on the importance of selection pressure and low dimensional weak learners to produce robust ensembles. / Vargas, Danilo Vasconcellos; Takano, Hirotaka; Murata, Junichi.
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion. 2013. p. 1755-1756.研究成果: 著書/レポートタイプへの貢献 › 会議での発言
}
TY - GEN
T1 - A study on the importance of selection pressure and low dimensional weak learners to produce robust ensembles
AU - Vargas, Danilo Vasconcellos
AU - Takano, Hirotaka
AU - Murata, Junichi
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84882432200&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882432200&partnerID=8YFLogxK
U2 - 10.1145/2464576.2480775
DO - 10.1145/2464576.2480775
M3 - Conference contribution
SN - 9781450319645
SP - 1755
EP - 1756
BT - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
ER -