Name
Title | ||
|---|---|---|
Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3056540.3064945 | PETRA |
Keywords | Field | DocType |
Extreme learning machine, missing values, imputation, Huntington's disease | Training set,Online machine learning,Data mining,Extreme learning machine,Computer science,Brute force,Artificial intelligence,Missing data,Imputation (statistics),Machine learning,Test set | Conference |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anton Akusok | 1 | 143 | 10.72 |
| Emil Eirola | 2 | 138 | 11.44 |
| Kaj-Mikael Björk | 3 | 148 | 16.40 |
| Yoan Miche | 4 | 1054 | 54.56 |
| Hans J. Johnson | 5 | 627 | 52.13 |
| Amaury Lendasse | 6 | 1876 | 126.03 |