short-paper

Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease

Authors:

Kaj-Mikael Björk,

Amaury LendasseAuthors Info & Claims

PETRA '17: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments

Pages 189 - 192

https://doi.org/10.1145/3056540.3064945

Published: 21 June 2017 Publication History

Abstract

This paper presents a novel procedure to train Extreme Learning Machine models on datasets with missing values. In effect, a separate model is learned to classify every sample in the test set, however, this is accomplished in an efficient manner which does not require accessing the training data repeatedly. Instead, a sparse structure is imposed on the input layer weights, which enables calculating the necessary statistics in the training phase. An application to predicting the progression of Huntington's disease from brain scans is presented. Experimental comparisons show promising results equivalent to the state of the art in machine learning with incomplete data.

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Cited By

Ganesh SChithambaram TKrishnan NVincent DKaliappan JSrinivasan K(2023)Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive ReviewDiagnostics10.3390/diagnostics1323359213:23(3592)Online publication date: 3-Dec-2023
https://doi.org/10.3390/diagnostics13233592
Bhachawat SShriram ESrinivasan KHu Y(2023)Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research DirectionsDiagnostics10.3390/diagnostics1302028813:2(288)Online publication date: 12-Jan-2023
https://doi.org/10.3390/diagnostics13020288
Cappelletti LFontana TDi Donato GDi Tucci LCasiraghi EValentini G(2020)Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-FillingComputers10.3390/computers90200379:2(37)Online publication date: 11-May-2020
https://doi.org/10.3390/computers9020037
Show More Cited By

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Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease

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PETRA '17: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments

June 2017

455 pages

ISBN:9781450352277

DOI:10.1145/3056540

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NSF: National Science Foundation
CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2017

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PETRA '17

PETRA '17: 10th International Conference on PErvasive Technologies Related to Assistive Environments

June 21 - 23, 2017

Island of Rhodes, Greece

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Cited By

Ganesh SChithambaram TKrishnan NVincent DKaliappan JSrinivasan K(2023)Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive ReviewDiagnostics10.3390/diagnostics1323359213:23(3592)Online publication date: 3-Dec-2023
https://doi.org/10.3390/diagnostics13233592
Bhachawat SShriram ESrinivasan KHu Y(2023)Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research DirectionsDiagnostics10.3390/diagnostics1302028813:2(288)Online publication date: 12-Jan-2023
https://doi.org/10.3390/diagnostics13020288
Cappelletti LFontana TDi Donato GDi Tucci LCasiraghi EValentini G(2020)Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-FillingComputers10.3390/computers90200379:2(37)Online publication date: 11-May-2020
https://doi.org/10.3390/computers9020037
Eirola EAkusok ABjörk KLendasse A(2018)Distance Estimation for Incomplete Data by Extreme Learning MachineProceedings of ELM-201710.1007/978-3-030-01520-6_18(203-209)Online publication date: 17-Oct-2018
https://doi.org/10.1007/978-3-030-01520-6_18

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