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

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