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

Anton Akusok, Emil Eirola, Kaj-Mikael Björk, Yoan Miche, Hans Johnson, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication10th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2017
Number of pages4
VolumePart F128530
PublisherACM - Association for Computing Machinery
Publication date21.06.2017
Pages189-192
ISBN (Electronic)978-1-4503-5227-7
DOIs
Publication statusPublished - 21.06.2017
MoE publication typeA4 Article in conference proceedings
Event10th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2017 - Island of Rhodes, Greece
Duration: 21.06.201723.06.2017

Keywords

  • 512 Business and Management

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