KERMIT -- Research Unit Knowledge-based Systems

Journal papers

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2024 (1)
2023 (2)
2022 (4)
2021 (12)
2020 (5)
2019 (7)
2018 (7)
2017 (10)
2016 (2)
2015 (1)
2014 (4)
2013 (7)
2012 (6)
2011 (6)
2010 (4)
2009 (2)
2008 (4)
2007 (1)

(4) A transitivity analysis of bipartite rankings in pairwise multi-class classification
W. Waegeman and B. De Baets
(2010) INFORMATION SCIENCES. 180, 4099-4117.

(3) On the role of cost-sensitive learning in multi-class brain-computer interfaces
D. Devlaminck, W. Waegeman, B. Wyns, G. Otte and P. Santens
(2010) BIOMEDICAL ENGINEERING. 55, 163-172.

(2) Learning intransitive reciprocal relations with kernel methods
T. Pahikkala, W. Waegeman, E. Tsivtsivadze, T. Salakoski and B. De Baets
(2010) EUROPEAN J. OPER. RES.. 206, 676-685.

(1) From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification
B. Slabbinck, W. Waegeman, P. Dawyndt, P. De Vos and B. De Baets
(2010) BMC BIOINFORMATICS. 11, 69.

Conference papers

All
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)

(8) A Genetic Takagi-Sugeno Fuzzy System for Fish Habitat Preference Modelling
S. Fukuda, N. Onikura, B. De Baets, W. Waegeman, A. Mouton, J. Nakajima, T. Mukai
(2010) PROCEEDINGS OF THE WORLD CONGRESS ON NATURE AND BIOLOGICALLY INSPIRED COMPUTING (NABIC).
Kitakyushu, Japan, 281-286. .

(7) From circular ordinal regression to multilabel classification
D. Devlaminck, W. Waegeman, B. Bauwens, B. Wyns, G. Otte, P. Santens
(2010) ECML 2010 WORKSHOP ON PREFERENCE LEARNING.
Barcelona, Spain, 14 pages. .

(6) Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss
K. Dembczynski,, W. Waegeman , W. Cheng and E. Hüllermeier
(2010) EUROPEAN CONFERENCE ON MACHINE LEARNING.
Barcelona, Spain, 280-295. Lecture Notes in Artificial Intelligence Vol. 6322 Part 1.

(5) Conditional Ranking on Relational Data
T. Pahikkala, W. Waegeman, A. Airola, T. Salakoski and B. De Baets
(2010) EUROPEAN CONFERENCE ON MACHINE LEARNING.
Barcelona, Spain, 499-518. Lecture Notes In Artificial Intelligence Vol. 6322 Part 2.

(4) Decomposition methods for multi-label learning of compositional data
J. Verwaeren, W. Waegeman and B. De Baets
(2010) ICML 2010 WORKSHOP ON LEARNING FROM MULTI-LABEL DATA.
Haifa, Israel, 8 pages. .

(3) On Label Dependence in Multi-label Classification
K. Dembczynski,, W. Waegeman, W. Cheng and E. Hüllermeier
(2010) ICML 2010 WORKSHOP ON LEARNING FROM MULTI-LABEL DATA.
Haifa, Israel, 8 pages. .

(2) Learning partial ordinal class memberships in a proportional odds setting
J. Verwaeren, W. Waegeman and B. De Baets
(2010) THE ANNUAL BENELUX CONFERENCE ON MACHINE LEARNING.
Leuven, Belgium, 8 pages. .

(1) Directional Predictions for 4-class BCI Data
D. Devlaminck, W. Waegeman, B. Bauwens, B. Wyns, G. Otte, L. Boullart, P. Santens
(2010) EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS.
Bruges, Belgium, 6 pages. .

Date	Title
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

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

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

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