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.
Recommended Citation
A. Akusok et al., "Brute-Force Missing Data Extreme Learning Machine for Predicting Huntington's Disease," ACM International Conference Proceeding Series, pp. 189 - 192, Association for Computing Machinery, Jun 2017.
The definitive version is available at https://doi.org/10.1145/3056540.3064945
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Extreme learning machine; Missing values, imputation, Huntington's disease
International Standard Book Number (ISBN)
978-145035227-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
21 Jun 2017
