Deep and interpretable regression models for ordinal outcomes

Kook, Lucas; Herzog, Lisa; Hothorn, Torsten; Dürr, Oliver; Sick, Beate

doi:10.1016/j.patcog.2021.108263

Cited by 24 publications

(17 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, the experimental findings demonstrated how a standard CCE together with CLM is sufficient to model ordinal structure of the label, without requiring the minimization of an ordinal loss (e.g., QWK). This is also in line with recent findings in the ordinal classification literature [44]. Moreover, the ordinal constraints allow the network to learn the characteristics that properly describe the quality of shotgun (i.e., wood grains), rather than other confounds/bias characteristics (e.g., geometry).…”

Section: Workpacesupporting

confidence: 86%

A novel deep ordinal classification approach for aesthetic quality control classification

Rosati

Romeo

Vargas

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking into account the increasing amount of data in this domain, the application of machine learning (ML) and deep learning (DL) techniques offers great opportunities to automatize the overall AQC process. State-of-the-art is mainly oriented to approach this problem with a nominal DL classification method which does not exploit the ordinal structure of the AQC task, thus not penalizing the error among distant AQC classes (which is a relevant aspect for the real use case). The paper introduces a DL ordinal methodology for the AQC classification. Differently from other deep ordinal methods, we combined the standard categorical cross-entropy with the cumulative link model and we imposed the ordinal constraint via the thresholds and slope parameters. Experimental results were performed for solving an AQC task on a novel image dataset originated from a specific company’s demand (i.e., aesthetic assessment of wooden stocks). We demonstrated how the proposed methodology is able to reduce misclassification errors (up to 0.937 quadratic weight kappa loss) among distant classes while overcoming other state-of-the-art deep ordinal models and reducing the bias factor related to the item geometry. The proposed DL approach was integrated as the main core of a DSS supported by Internet of Things (IoT) architecture that can support the human operator by reducing up to 90% the time needed for the qualitative analysis carried out manually in this specific domain.

show abstract

Section: Workpacesupporting

confidence: 86%

A novel deep ordinal classification approach for aesthetic quality control classification

Rosati

Romeo

Vargas

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The transformation function (upper right panel) transforms the complex, bimodal distribution of (lower panel) to , the standard minimum extreme value distribution (upper left panel). An analogous figure for ordinal outcomes is published in Kook et al ( 2022 , Fig. 1).…”

Section: Introductionmentioning

confidence: 66%

“…The implementation of the interval censored log-likelihood for ordinal TMs was taken from Kook et al. ( 2022 ) and we used SGD with the Adam optimizer (Kingma and Ba 2015 ) with learning rate , batch size 250 and 200 epochs. Parameters were initialized with the maximum likelihood estimate for obtained via tram::Polr() (Hothorn 2020 ).…”

Section: A Notationmentioning

confidence: 99%

See 1 more Smart Citation

Distributional anchor regression

2022

Self Cite

View full text Add to dashboard Cite

Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general requires strong assumptions on the data generating process (DGP). In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors. Anchor regression models, introduced by Rothenhäusler et al. (J R Stat Soc Ser B 83(2):215–246, 2021. 10.1111/rssb.12398), protect against distributional shifts in the test data by employing causal regularization. However, so far anchor regression has only been used with a squared-error loss which is inapplicable to common responses such as censored continuous or ordinal data. Here, we propose a distributional version of anchor regression which generalizes the method to potentially censored responses with at least an ordered sample space. To this end, we combine a flexible class of parametric transformation models for distributional regression with an appropriate causal regularizer under a more general notion of residuals. In an exemplary application and several simulation scenarios we demonstrate the extent to which OOD generalization is possible.

show abstract

“…For the time being, we leave a more runtime efficient version of our framework to future implementation and research. Finally, we consider the extension of our framework to allow for other Normalizing Flow types, suitable for count or ordinal data as proposed by Kook et al (2021), as an interesting enhancement. 3127 [3.6087, 7.9982] 10.655 [10.4204, 11.3639] 25.0991 [22.4859, 25.8206] 6.1083 [5.6999, 7.4556] 14.877 [14.1427, 19.3816] The table shows median CRPS scores across all folds, with interquartile-range values in parentheses, i.e., q0.…”

Section: Conclusion Limitations and Futurementioning

confidence: 99%

Distributional Gradient Boosting Machines

März¹,

Kneib²

2022

Preprint

View full text Add to dashboard Cite

We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable as a function of covariates. Our likelihood-based approach allows us to either model all conditional moments of a parametric distribution, or to approximate the conditional cumulative distribution function via Normalizing Flows. As underlying computational backbones, our framework is based on XGBoost and LightGBM. Modelling and predicting the entire conditional distribution greatly enhances existing tree-based gradient boosting implementations, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. Empirical results show that our framework achieves state-of-the-art forecast accuracy.

show abstract

Deep and interpretable regression models for ordinal outcomes

Cited by 24 publications

References 9 publications

A novel deep ordinal classification approach for aesthetic quality control classification

A novel deep ordinal classification approach for aesthetic quality control classification

Distributional anchor regression

Distributional Gradient Boosting Machines

Contact Info

Product

Resources

About