Research Unit Knowledge-based Systems
Mathematical and Computational Modelling in the Information Age
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Journal papers
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2007 (1)
(6)
A kernel-based framework for learning graded relations from data
W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets(2012) IEEE TRANSACTIONS ON FUZZY SYSTEMS. 20, 1090-1101.
(5)
The logistic curve as a tool to describe the daily ruminal pH pattern and its link with milk fatty acids
E. Colman, B. Thas, W. Waegeman, B. De Baets and V. Fievez(2012) J. DAIRY SCIENCE. 95, 5845-5865.
(4)
On label dependence and loss minimization in multi-label classification
K. Dembczynski, W. Waegeman, W. Cheng and E. Hüllermeier(2012) MACHINE LEARNING. 88, 5-45.
(3)
Towards a reliable evaluation of forecasting systems for plant diseases: A case study of Fusarium head blight
S. Landschoot, W. Waegeman, K. Audenaert, J. Vandepitte, G. Haesaert and B. De Baets(2012) PLANT DISEASE. 96, 889-896.
(2)
An empirical analysis of explanatory variables affecting Fusarium head blight infection and deoxynivalenol content in wheat
S. Landschoot, W. Waegeman, K. Audenaert, J. Vandepitte, J.M. Baetens, B. De Baets and G. Haesaert(2012) JOURNAL OF PLANT PATHOLOGY. 94, 135-147.
(1)
Learning partial ordinal class memberships with kernel-based proportional odds models
J. Verwaeren, W. Waegeman and B. De Baets(2012) COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 56, 928-942.
Conference papers
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2022 (1)
2019 (1)
2018 (1)
2017 (1)
2016 (2)
2015 (1)
2014 (1)
2013 (1)
2012 (4)
2011 (6)
2010 (8)
2009 (4)
2008 (2)
2007 (2)
2006 (2)
2023 (1)
(4)
Label ranking with partial abstention based on thresholded probabilistic models
W. Cheng, E. Hüllermeier , W. Waegeman and V. Welker(2012) ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).Lake Tahoe, Nevada, USA, 9 pages. .
(3)
Learning Monadic and Dyadic Relations: Three Case Studies in Systems Biology
M. Stock, T. Pahikkala, A. Airola, T. Salakoski, B. De Baets and W. Waegeman(2012) ECML WORKSHOP ON LEARNING AND DISCOVERY IN SYMBOLIC SYSTEMS BIOLOGY.Bristol, UK, 12 pages. .
(2)
An analysis of chaining in multi-label classification
K. Dembczynski, W. Waegeman and E. Hüllermeier(2012) EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE.Montpellier, France, 294-299. Frontiers in Artificial Intelligence and Applications 242.
(1)
F-Measure Maximization in Multilabel Classification
W. Cheng, K. Dembczynski, E. Hüllermeier, A. Jaroszewicz and W. Waegeman(2012) JRC DATA MINING CONTEST., 8 pages. .
Editor
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Awards
Date
Title
W. Waegeman
18/08/2014
2015 IEEE CIS Outstanding TFS Paper Award
27/08/2012
Best Paper Award for Willem Waegeman
17/08/2012
Second Winner Award for Willem Waegeman
Students
Phd
MSc
PhD Students
Name
Research Topic
Dewolf Nicolas
Machine learning methods for time series data
MSc Students
Name
Research Topic
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Graduates
Phd
MSc
PhD Students
Name
Title
Date
D. Iliadis
Automated multi-target prediction with two-branch neural networks
17/11/2023
T. Mortier
Efficient algorithms for set-valued prediction in classification
23/06/2023
M. Gasparyan
Model reduction and parameter estimation for kinetic models of biochemical reaction networks
19/01/2023
F. Mey
Integration of synthetic biology, systems biology and machine learning for the production of chitooligosaccharides in Escherichia coli
18/01/2023
J. Clauwaert
Deep learning techniques for genome processing and annotation tasks in prokaryotes
24/09/2020
P. Rubbens
Machine learning approaches for microbial flow cytometry at the single-cell and community level
24/09/2019
C. Papagiannopoulou
A data-guided insight into global climate-vegetation dynamics
05/10/2018
M. Stock
Exact and efficient algorithms for pairwise learning
21/04/2017
MSc Students
Name
Title
Date
N. Tourne
Two-branch neural networks for predicting protein-DNA interaction
2022 - 2023
J.-H. Nowé
Countering illegal timbering by using multimodal machine learning
2022 - 2023
Y. Van Laere
Detection of 5mC modification in Nanopore sequencing data using deep learning
2021 - 2022
E. Lorrez
Quantifying the environmental controls on African tropical forest dynamics through using a Granger causality framework
2020 - 2021
K. Zhang
Drug-target interaction prediction using multi-target prediction methods
2020 - 2021
L. Theunissen
A comparison of flat and hierarchical classification for automatic annotation of single-cell transcriptomics data
2020 - 2021
T. Willaert
Improving classification by rejection of high epistemic uncertainty points
2020 - 2021
L. Pollaris
Protein secondary structure prediction using transformer networks
2019 - 2020
B. De Saedeleer
Combatting illegal timber trade using chemical fingerprints: the power of mathematics and mass spectrometry
2019 - 2020
B. Verfaillie
Pattern recognition in raman spectroscopy data for a faster labelling of subjects in multiple domains
2019 - 2020
L. Davey
Using artificial neural networks to uncover features in promoter sequences responsible for nonorthogonality in E. Coli
2019 - 2020
G. Tjon
Automative drinking water monitoring using flow cytometry data
2019 - 2020
S. Top
Vertical farming of lettuce: the influence of rhizosphere bacteria and substrates
2018 - 2019
K. D'haeyer
Towards a data-driven identification of the microbial "Rammanome"
2018 - 2019
G. De Clercq
Deep learning for classification of DNA functional sequences
2018 - 2019
N. Goeders
Fight the illegal wood trade through chemical fingerprints : The power of mathematics and mass spectrometry
2018 - 2019
B. De Clercq
Forecasting tidal surge in the Lower Sea Scheldt using machine learning techniques
2018 - 2019
M. Van Haeverbeke
Detection of m6a modifications in native RNA using Oxford Nanopore Technology
2018 - 2019
M. Misonne
Prediction of RNA polymerase-DNA interactions in Escherichia Coli
2017 - 2018
R. Ingels
Understanding vegetation anomalies with machine learning methods
2017 - 2018
A. De Graeve
Detecting climate drivers for vegetation extremes
2017 - 2018
T. Vanlerberghe
Hierarchical multi-label classification of food products
2017 - 2018
T. Mortier
Modeling of climate-vegetation dynamics using machine learning techniques in a non-linear Granger causality framework
2016 - 2017
X. Yin
Discovering relationships in climate-vegetation dynamics using dynamic feature selection techniques
2016 - 2017
D. Schaumont
Een integratieve benadering gebaseerd op random forest voor de verbeterde predictie van exon-intron juncties
2016 - 2017
C. Zhang
Visualization and unsupervised learning of flow cytometry data for bacterial identication
2016 - 2017
S. Decubber
Spatiotemporal optimization of Granger causality methods for climate change attribution
2016 - 2017
J. Heyse
Development and application of single-cell analysis tools for the study of sympatric bacterial populations
2016 - 2017
L. Bodyn
Exploration of deep autoencoders for collaborative filtering on cooking recipes
2016 - 2017
L. Tilleman
In silico engineering van cytochroom P450 via machine learning technieken
2015 - 2016
F. Ramon
A data-driven analysis of ingredients in cooking recipes
2015 - 2016
W.K. Tsang
Assessing pathogen invasion based on community evenness and metabolic similarity
2014 - 2015
J. De Reu
Analyse van voorspellingsmodellen voor aardappelziekten en hun toepasbaarheid in Vlaanderen
2014 - 2015
M. De Clercq
Prediction of ingredient combinations using machine learning techniques
2013 - 2014
A. De Paepe
The prediction of interaction between mRNA and miRNA using machine learning techniques
2012 - 2013
M. Stock
Learning pairwise relations in bioinformatics: three case studies
2011 - 2012
J. Vandepitte
Voorspellen van Fusarium spp. aanwezigheid en DON concentraties in wintertarwe met machine learning technieken
2009 - 2010