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Torsten Hothorn

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

JMLR Journal 2015 Journal Article

partykit: A Modular Toolkit for Recursive Partytioning in R

Torsten Hothorn
Achim Zeileis

The R package partykit provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree- structured regression and classification models. The functionality encompasses: (a) basic infrastructure for representing trees (inferred by any algorithm) so that unified print / plot / predict methods are available; (b) dedicated methods for trees with constant fits in the leaves (or terminal nodes) along with suitable coercion functions to create such trees (e.g., by rpart, RWeka, PMML); (c) a reimplementation of conditional inference trees ( ctree, originally provided in the party package); (d) an extended reimplementation of model-based recursive partitioning ( mob, also originally in party) along with dedicated methods for trees with parametric models in the leaves. Here, a brief overview of the package and its design is given while more detailed discussions of items (a)—(d) are available in vignettes accompanying the package. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2015. ( edit, beta )

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JMLR Journal 2010 Journal Article

Model-based Boosting 2.0

Torsten Hothorn
Peter Bühlmann
Thomas Kneib
Matthias Schmid
Benjamin Hofner

We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2010. ( edit, beta )

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AIIM Journal 2003 Journal Article

Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy

Torsten Hothorn
Berthold Lausen

Diagnosis based on medical image data is common in medical decision making and clinical routine. We discuss a strategy to derive a classifier with good performance on clinical image data and to justify the properties of the classifier by an adapted simulation model of image data. We focus on the problem of classifying eyes as normal or glaucomatous based on 62 routine explanatory variables derived from laser scanning images of the optic nerve head. As learning sample we use a case-control study of 98 normal and 98 glaucomatous subjects matched by age and sex. Aggregating multiple unstable classifiers allows substantial reduction of misclassification error in many applications and bench mark problems. We investigate the performance of various classifiers for the clinical learning sample as well as for a simulation model of eye morphologies. Bagged classification trees (bagged-CTREE) are compared to single classification trees and linear discriminant analysis (LDA). We additionally compare three estimators of misclassification error: 10-fold cross-validation, the 0. 632+ bootstrap and the out-of-bag estimate. In summary, the application of our strategy of a knowledge-based decision support shows that bagged classification trees perform best for glaucoma classification.

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