Ensemble Delta Test-Extreme Learning Machine (Dt-Elm) for Regression
Abstract
Extreme Learning Machine (Elm) Has Shown its Good Performance in Regression Applications with a Very Fast Speed. But There is Still a Difficulty to Compromise between Better Generalization Performance and Smaller Complexity of the Elm (A Number of Hidden Nodes). This Paper Proposes a Method Called Delta Test-Elm (Dt-Elm), Which Operates in an Incremental Way to Create Less Complex Elm Structures and Determines the Number of Hidden Nodes Automatically. It Uses Bayesian Information Criterion (Bic) as Well as Delta Test (Dt) to Restrict the Search as Well as to Consider the Size of the Network and Prevent overfitting. Moreover, Ensemble Modeling is Used on Different Dt-Elm Models and It Shows Good Test Results in Experiments Section. © 2014.
Recommended Citation
Q. Yu et al., "Ensemble Delta Test-Extreme Learning Machine (Dt-Elm) for Regression," Neurocomputing, vol. 129, pp. 153 - 158, Elsevier, Apr 2014.
The definitive version is available at https://doi.org/10.1016/j.neucom.2013.08.041
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Bayesian information criterion; Delta test; Ensemble modeling; Extreme learning machine; Incremental learning
International Standard Serial Number (ISSN)
1872-8286; 0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
10 Apr 2014
