Robin Senge
Robin Senge
inovex GmbH
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Cited by
Cited by
Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
R Senge, S Bösner, K Dembczyński, J Haasenritter, O Hirsch, ...
Information Sciences 255, 16-29, 2014
Dependent binary relevance models for multi-label classification
E Montanes, R Senge, J Barranquero, JR Quevedo, JJ del Coz, ...
Pattern Recognition 47 (3), 1494-1508, 2014
Comparing fuzzy partitions: A generalization of the rand index and related measures
E Hullermeier, M Rifqi, S Henzgen, R Senge
IEEE Transactions on Fuzzy Systems 20 (3), 546-556, 2011
Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction
D Heider, R Senge, W Cheng, E Hüllermeier
Bioinformatics 29 (16), 1946-1952, 2013
Top-down induction of fuzzy pattern trees
R Senge, E Hüllermeier
IEEE Transactions on Fuzzy Systems 19 (2), 241-252, 2010
On the Problem of Error Propagation in Classifier Chains for Multi-Label Classification⋆
R Senge, JJ del Coz, E Hüllermeier
Data Analysis, Machine Learning and Knowledge Discovery, 163-170, 2014
Distributional Regression for Demand Forecasting in e-grocery
H Jahnke, M Ulrich, R Pesch, R Senge, R Langrock
European Journal of Operational Research, 2019
Rectifying classifier chains for multi-label classification
R Senge, JJ del Coz, E Hüllermeier
Lernen, Wissen, Adaption 2013, 162-169, 2013
Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams
A Shaker, R Senge, E Hüllermeier
Information Science, 34-45, 2013
Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification
M Riemenschneider, R Senge, U Neumann, E Hüllermeier, D Heider
BioData mining 9, 1-6, 2016
Classification-based model selection in retail demand forecasting
M Ulrich, H Jahnke, R Langrock, R Pesch, R Senge
International Journal of Forecasting 38 (1), 209-223, 2022
Fast fuzzy pattern tree learning for classification
R Senge, E Huellermeier
IEEE Transactions on Fuzzy Systems 23 (6), 2024-2033, 2015
Pattern trees for regression and fuzzy systems modeling
R Senge, E Hüllermeier
International Conference on Fuzzy Systems, 1-7, 2010
Multivariate modeling to identify patterns in clinical data: the example of chest pain
O Hirsch, S Bösner, E Hüllermeier, R Senge, K Dembczynski, ...
BMC medical research methodology 11, 1-10, 2011
José Barranquero, José Ramón Quevedo, Juan José del Coz, and Eyke Hüllermeier. 2014. Dependent binary relevance models for multi-label classification
E Montañés, R Senge
Pattern Recognition 47 (3), 1494-1508, 2014
Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry
M Nasiri, E Hüllermeier, R Senge, E Lughofer
Proceedings IFSA–2011, World Congress of the International Fuzzy Systems …, 2011
Fuzzy Pattern Trees as an Alternative to Rule-based Fuzzy Systems: Knowledge-driven, Data-driven and Hybrid Modeling of Color Yield in Polyester Dyeing
M Nasiri, T Fober, R Senge, E Hüllermeier
IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, 2013
Machine learning methods for fuzzy pattern tree induction
R Senge
Philipps-Universität Marburg, 2014
Accuracy of diagnostic tests for feline infectious peritonitis (FIP) in cats with body cavity effusion
S Held, M König, HP Hamann, R Senge, E Hüllermeier, R Neiger
Proceedings of the European College of Veterinary Internal Medicine Congress …, 2011
Diagnosis in context-broadening the perspective
J Haasenritter, A Viniol, A Becker, S Bösner, E Hüllermeier, R Senge, ...
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen 107 (9 …, 2012
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