A Preliminary Study of Diversity in Extreme Learning Machines Ensembles

被引：0

作者：

Perales-Gonzalez, Carlos ^{[1
]}

Carbonero-Ruz, Mariano ^{[1
]}

Becerra-Alonso, David ^{[1
]}

Fernandez-Navarro, Francisco ^{[1
]}

机构：

[1] Univ Loyola Andalucia, Dept Quantitat Methods, Seville, Spain

来源：

HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018) | 2018年 / 10870卷

关键词：

Extreme learning machine; Diversity; Machine learning; Ensemble; AdaBoost; REGRESSION;

D O I：

10.1007/978-3-319-92639-1_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the neural network version of Extreme Learning Machine (ELM) is used as a base learner for an ensemble meta algorithm which promotes diversity explicitly in the ELM loss function. The cost function proposed encourages orthogonality (scalar product) in the parameter space. Other ensemble-based meta-algorithms from AdaBoost family are used for comparison purposes. Both accuracy and diversity presented in our proposal are competitive, thus reinforcing the idea of introducing diversity explicitly.

引用

页码：302 / 314

页数：13

共 50 条

[1] Ensembles of learning machines
Valentini, G
Masulli, R
NEURAL NETS, 2002, 2486 : 3 - 19
[2] Spatially regularized semisupervised Ensembles of Extreme Learning Machines for hyperspectral image segmentation
Ayerdi, Borja
Marques, Ion
Grana, Manuel
NEUROCOMPUTING, 2015, 149 : 373 - 386
[3] Ensembles of Evolutionary Extreme Learning Machines Through Differential Evolution and Fitness Sharing
de Lima, Tiago. P. F.
Ludermir, Teresa. B.
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 2677 - 2682
[4] EXPLOITING DIVERSITY OF NEURAL NETWORK ENSEMBLES BASED ON EXTREME LEARNING MACHINE
Garcia-Laencina, Pedro J.
Roca-Gonzalez, Jose-Luis
Bueno-Crespo, Andres
Sancho-Gomez, Jose-Luis
NEURAL NETWORK WORLD, 2013, 23 (05) : 395 - 409
[5] Extreme Learning Machines
Cambria, Erik
Huang, Guang-Bin
IEEE INTELLIGENT SYSTEMS, 2013, 28 (06) : 30 - 31
[6] Extreme learning machines
MIT Media Laboratory, Singapore
不详
不详
不详
不详
不详
不详
不详
不详
不详
不详
不详
IEEE Intell. Syst., 2013, 6 (30-59):
[7] Extreme ensemble of extreme learning machines
Mansoori, Eghbal G.
Sara, Massar
STATISTICAL ANALYSIS AND DATA MINING, 2021, 14 (02) : 116 - 128
[8] An experimental study on stability and generalization of extreme learning machines
Fu, Aimin
Dong, Chunru
Wang, Laisheng
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2015, 6 (01) : 129 - 135
[9] An experimental study on stability and generalization of extreme learning machines
Aimin Fu
Chunru Dong
Laisheng Wang
International Journal of Machine Learning and Cybernetics, 2015, 6 : 129 - 135
[10] Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting
Alves Ribeiro, Victor Henrique
Reynoso-Meza, Gilberto
Siqueira, Hugo Valadares
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 95

← 1 2 3 4 5 →