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.
Recommended Citation
B. Chi et al., "A Multi-ELM Model for Incomplete Data," ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 541 - 546, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Jan 2021.
The definitive version is available at https://doi.org/10.14428/esann/2021.ES2021-162
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-287587082-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, All rights reserved.
Publication Date
01 Jan 2021
