TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Kook, Lucas
A1  - Herzog, Lisa
A1  - Hothorn, Torsten
A1  - Dürr, Oliver
A1  - Sick, Beate
T1  - Deep and interpretable regression models for ordinal outcomes
JF  - Pattern Recognition
N2  - Outcomes with a natural order commonly occur in prediction problems and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning (DL) models are state-of-the-art for image classification tasks but frequently treat ordinal outcomes as unordered and lack interpretability. In contrast, classical ordinal regression models consider the outcome’s order and yield interpretable predictor effects but are limited to tabular data. We present ordinal neural network transformation models (ontrams), which unite DL with classical ordinal regression approaches. ontrams are a special case of transformation models and trade off flexibility and interpretability by additively decomposing the transformation function into terms for image and tabular data using jointly trained neural networks. The performance of the most flexible ontram is by definition equivalent to a standard multi-class DL model trained with cross-entropy while being faster in training when facing ordinal outcomes. Lastly, we discuss how to interpret model components for both tabular and image data on two publicly available datasets.
KW  - Deep learning
KW  - Interpretability
KW  - Distributional regression
KW  - Ordinal regression
KW  - Transformation models
Y1  - 2022
SN  - 1873-5142
SS  - 1873-5142
U6  - https://doi.org/10.1016/j.patcog.2021.108263
DO  - https://doi.org/10.1016/j.patcog.2021.108263
N1  - Corresponding Author: Beate Sick
VL  - Vol. 122
PB  - Elsevier
ER  -