2020 |
Shaker, Mohammad Hossein; Hüllermeier, Eyke Aleatoric and Epistemic Uncertainty with Random Forests Artikel arXiv:2001.00893 [cs, stat], 2020, (arXiv: 2001.00893). @article{shaker_aleatoric_2020, title = {Aleatoric and Epistemic Uncertainty with Random Forests}, author = {Mohammad Hossein Shaker and Eyke Hüllermeier}, url = {http://arxiv.org/abs/2001.00893}, year = {2020}, date = {2020-01-01}, urldate = {2020-01-14}, journal = {arXiv:2001.00893 [cs, stat]}, abstract = {Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learnertextquoterights aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.}, note = {arXiv: 2001.00893}, keywords = {}, pubstate = {published}, tppubtype = {article} } Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learnertextquoterights aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose. |
2019 |
Holst, Christoph-Alexander; Lohweg, Volker Feature fusion to increase the robustness of machine learners in industrial environments Artikel at - Automatisierungstechnik, 67 (10), S. 853–865, 2019. @article{holst2019feature, title = {Feature fusion to increase the robustness of machine learners in industrial environments}, author = {Christoph-Alexander Holst and Volker Lohweg}, doi = {10.1515/auto-2019-0028}, year = {2019}, date = {2019-01-01}, journal = {at - Automatisierungstechnik}, volume = {67}, number = {10}, pages = {853--865}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Pfannschmidt, Lukas; Jakob, Jonathan; Biehl, Michael; Tino, Peter; Hammer, Barbara Feature Relevance Bounds for Ordinal Regression Artikel arXiv preprint arXiv:1902.07662, 2019. @article{pfannschmidt2019feature, title = {Feature Relevance Bounds for Ordinal Regression}, author = {Lukas Pfannschmidt and Jonathan Jakob and Michael Biehl and Peter Tino and Barbara Hammer}, year = {2019}, date = {2019-01-01}, journal = {arXiv preprint arXiv:1902.07662}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Brinkrolf, Johannes; Hammer, Barbara Time integration and reject options for probabilistic output of pairwise LVQ Artikel Neural Computing and Applications, S. 1–14, 2019. @article{brinkrolf2019time, title = {Time integration and reject options for probabilistic output of pairwise LVQ}, author = {Johannes Brinkrolf and Barbara Hammer}, year = {2019}, date = {2019-01-01}, journal = {Neural Computing and Applications}, pages = {1--14}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Bifet, Albert; Hammer, Barbara; Schleif, Frank-Michael Streaming data analysis, concept drift and analysis of dynamic data sets Artikel Proc. European Symposium on Artificial Neural Networks, 2019. @article{bifet2019streaming, title = {Streaming data analysis, concept drift and analysis of dynamic data sets}, author = {Albert Bifet and Barbara Hammer and Frank-Michael Schleif}, year = {2019}, date = {2019-01-01}, journal = {Proc. European Symposium on Artificial Neural Networks}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Göpfert, Christina; Ben-David, Shai; Bousquet, Olivier; Gelly, Sylvain; Tolstikhin, Ilya; Urner, Ruth When can unlabeled data improve the learning rate? Artikel arXiv preprint arXiv:1905.11866, 2019. @article{gopfert2019can, title = {When can unlabeled data improve the learning rate?}, author = {Christina Göpfert and Shai Ben-David and Olivier Bousquet and Sylvain Gelly and Ilya Tolstikhin and Ruth Urner}, year = {2019}, date = {2019-01-01}, journal = {arXiv preprint arXiv:1905.11866}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Eiteneuer, Benedikt; Hranisavljevic, Nemanja; Niggemann, Oliver Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder. Artikel International Conference on Industrial Technology, 2019. @article{eiteneuer2019dimensionality, title = {Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder.}, author = {Benedikt Eiteneuer and Nemanja Hranisavljevic and Oliver Niggemann}, year = {2019}, date = {2019-01-01}, journal = {International Conference on Industrial Technology}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
