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Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles
[Zeitschriftenartikel]
Abstract Existing ordinal trees and random forests typically use scores that are assigned to the ordered categories, which implies that a higher scale level is used. Versions of ordinal trees are proposed that take the scale level seriously and avoid the assignment of artificial scores. The construction prin... mehr
Existing ordinal trees and random forests typically use scores that are assigned to the ordered categories, which implies that a higher scale level is used. Versions of ordinal trees are proposed that take the scale level seriously and avoid the assignment of artificial scores. The construction principle is based on an investigation of the binary models that are implicitly used in parametric ordinal regression. These building blocks can be fitted by trees and combined in a similar way as in parametric models. The obtained trees use the ordinal scale level only. Since binary trees and random forests are constituent elements of the proposed trees, one can exploit the wide range of binary trees that have already been developed. A further topic is the potentially poor performance of random forests, which seems to have been neglected in the literature. Ensembles that include parametric models are proposed to obtain prediction methods that tend to perform well in a wide range of settings. The performance of the methods is evaluated empirically by using several data sets.... weniger
Thesaurusschlagwörter
Skalenkonstruktion; Modell; Regression
Klassifikation
Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Freie Schlagwörter
recursive partitioning; trees; random forests; ensemble methods; ordinal regression; Vorwahl-Querschnitt (GLES 2013) (ZA5700 v2.0.0)
Sprache Dokument
Englisch
Publikationsjahr
2022
Seitenangabe
S. 241-263
Zeitschriftentitel
Journal of Classification, 39 (2022) 2
DOI
https://doi.org/10.1007/s00357-021-09406-4
ISSN
1432-1343
Status
Veröffentlichungsversion; begutachtet (peer reviewed)