Conference proceeding
Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease
Proceedings of the 10th International Conference on pervasive technologies related to assistive environments, Vol.128530, pp.189-192
PETRA '17
06/21/2017
DOI: 10.1145/3056540.3064945
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.
Details
- Title: Subtitle
- Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease
- Creators
- Anton AkusokEmil EirolaKaj-Mikael BjörkYoan MicheHans JohnsonAmaury Lendasse
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 10th International Conference on pervasive technologies related to assistive environments, Vol.128530, pp.189-192
- Series
- PETRA '17
- DOI
- 10.1145/3056540.3064945
- Publisher
- ACM
- Language
- English
- Date published
- 06/21/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Industrial and Systems Engineering; The Iowa Institute for Biomedical Imaging; The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
- Record Identifier
- 9984221730802771
Metrics
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