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Journal of Classification
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22-01-2019

Ordinal Forests

Author: Roman Hornung

Published in: Journal of Classification | Issue 1/2020

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Abstract

The ordinal forest method is a random forest–based prediction method for ordinal response variables. Ordinal forests allow prediction using both low-dimensional and high-dimensional covariate data and can additionally be used to rank covariates with respect to their importance for prediction. An extensive comparison study reveals that ordinal forests tend to outperform competitors in terms of prediction performance. Moreover, it is seen that the covariate importance measure currently used by ordinal forest discriminates influential covariates from noise covariates at least similarly well as the measures used by competitors. Several further important properties of the ordinal forest algorithm are studied in additional investigations. The rationale underlying ordinal forests of using optimized score values in place of the class values of the ordinal response variable is in principle applicable to any regression method beyond random forests for continuous outcome that is considered in the ordinal forest method.
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Metadata
Title
Ordinal Forests
Author
Roman Hornung
Publication date
22-01-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2020
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-018-9302-x

Other articles of this Issue 1/2020

Editorial: Journal of Classification Vol. 37-1

Variable Selection for Mixed Data Clustering: Application in Human Population Genomics

On Cesáro Averages for Weighted Trees in the Random Forest

An Algorithm for Ordinal Classification Based on Pairwise Comparison

Clustering Large Datasets by Merging K-Means Solutions

Clique-Based Method for Social Network Clustering

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