A Multi-ELM Model for Incomplete Data

Baichuan Chi; Amaury Lendasse; Edward Ratner; Renjie Hu

doi:10.14428/esann/2021.ES2021-162

A Multi-ELM Model for Incomplete Data

Baichuan Chi, Amaury Lendasse, Edward Ratner, Renjie Hu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a novel model of Extreme Learning Machines (ELMs) for incomplete data. ELMs are fast accurate randomized neural networks. Nevertheless ELM can only be applied on the complete dataset. Therefore, a novel Multi-ELM Model for incomplete data is proposed, consisting of multiple secondary ELMs and one primary ELM. The secondary ELMs are approximating the primary ELM's hidden neurons' outputs for the data with missing values. As summarized in the experimental Section, this model can be applied on data with any missing patterns, without using imputations and can outperform the traditional imputation methods within a reasonable fraction of missing values, as it avoids the noises introduced by imputations.

Original language	English (US)
Title of host publication	ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	541-546
Number of pages	6
ISBN (Electronic)	9782875870827
DOIs	https://doi.org/10.14428/esann/2021.ES2021-162
State	Published - 2021
Event	29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021 - Virtual, Online, Belgium Duration: Oct 6 2021 → Oct 8 2021

Publication series

Name	ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference	29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021
Country/Territory	Belgium
City	Virtual, Online
Period	10/6/21 → 10/8/21

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Access to Document

10.14428/esann/2021.ES2021-162

Cite this

Chi, B., Lendasse, A., Ratner, E., & Hu, R. (2021). A Multi-ELM Model for Incomplete Data. In ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 541-546). (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication. https://doi.org/10.14428/esann/2021.ES2021-162

A Multi-ELM Model for Incomplete Data. / Chi, Baichuan; Lendasse, Amaury; Ratner, Edward et al.
ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2021. p. 541-546 (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chi, B, Lendasse, A, Ratner, E & Hu, R 2021, A Multi-ELM Model for Incomplete Data. in ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, pp. 541-546, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Virtual, Online, Belgium, 10/6/21. https://doi.org/10.14428/esann/2021.ES2021-162

Chi B, Lendasse A, Ratner E, Hu R. A Multi-ELM Model for Incomplete Data. In ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2021. p. 541-546. (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). doi: 10.14428/esann/2021.ES2021-162

Chi, Baichuan ; Lendasse, Amaury ; Ratner, Edward et al. / A Multi-ELM Model for Incomplete Data. ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2021. pp. 541-546 (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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A Multi-ELM Model for Incomplete Data

Abstract

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