Ensembles of learning machines

被引：0

作者：

Valentini, G ^{[1
]}

Masulli, R

机构：

[1] INFM, I-16146 Genoa, Italy

[2] Univ Genoa, DISI, I-16146 Genoa, Italy

[3] Univ Pisa, Dipartimento Informat, I-56125 Pisa, Italy

来源：

NEURAL NETS | 2002年 / 2486卷

关键词：

ensemble methods; combining multiple learners;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

引用

页码：3 / 19

页数：17

共 50 条

[41] Learning Ensembles in the Presence of Imbalanced Classes
Saadallah, Amal
Piatkowski, Nico
Finkeldey, Felix
Wiederkehr, Petra
Morik, Katharina
ICPRAM: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS, 2019, : 866 - 873
[42] Imbalance learning using heterogeneous ensembles
Zefrehi, Hossein Ghaderi
Altincay, Hakan
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 142
[43] Compact dual ensembles for active learning
Mandvikar, A
Liu, H
Motoda, H
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2004, 3056 : 293 - 297
[44] Neural Network Ensembles in Reinforcement Learning
Stefan Faußer
Friedhelm Schwenker
Neural Processing Letters, 2015, 41 : 55 - 69
[45] Learning Collaboration in Reactive Agents Ensembles
Berariu, Tudor
Florea, Adina Magda
2015 20TH INTERNATIONAL CONFERENCE ON CONTROL SYSTEMS AND COMPUTER SCIENCE, 2015, : 329 - 336
[46] Dynamic Weighting Ensembles for Incremental Learning
Yang, Xinzhu
Yuan, Bo
Liu, Wenhuang
PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2, 2009, : 98 - 102
[47] On selective learning in stochastic stepwise ensembles
Chun-Xia Zhang
Sang-Woon Kim
Jiang-She Zhang
International Journal of Machine Learning and Cybernetics, 2020, 11 : 217 - 230
[48] Active Learning with Adaptive Heterogeneous Ensembles
Lu, Zhenyu
Wu, Xindong
Bongard, Josh
2009 9TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 2009, : 327 - 336
[49] Negative Correlation Learning for Classification Ensembles
Wang, Shuo
Chen, Huanhuan
Yao, Xin
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010, 2010,
[50] PREDICTIVE LEARNING VIA RULE ENSEMBLES
Frieman, Jerome H.
Popescu, Bogdan E.
ANNALS OF APPLIED STATISTICS, 2008, 2 (03): : 916 - 954

← 1 2 3 4 5 →