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Journal of Classification
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05-06-2020

Editorial: Journal of Classification Vol. 37-1

Author: Douglas L. Steinley

Published in: Journal of Classification | Issue 1/2020

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Excerpt

The first issue of 2020 has fourteen articles that cover a broad set of topics. The first article, by Roman Hornung, develops ordinal forests as a type of random forest used for predicting ordinal response variables. As in the vein of standard random forest, Hornung develops a variable importance assessment to aid in understanding differential prediction rates for covariates. This is a welcome addition to span the standard regression trees (continuous data) and classification trees (binary data), and is likely to see use in the social and behavioral sciences where ordinal data are commonly seen. …
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Metadata
Title
Editorial: Journal of Classification Vol. 37-1
Author
Douglas L. Steinley
Publication date
05-06-2020
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/s00357-020-09365-2

Other articles of this Issue 1/2020

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

Clique-Based Method for Social Network Clustering

A Nonparametric Estimator of Bivariate Quantile Residual Life Model with Application to Tumor Recurrence Data Set

Core Clustering as a Tool for Tackling Noise in Cluster Labels

Clustering Large Datasets by Merging K-Means Solutions

On Cesáro Averages for Weighted Trees in the Random Forest

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