KERMIT -- Research Unit Knowledge-based Systems

Journal papers

All
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)

(85) A comparison of embedding aggregation strategies in drug-target interaction prediction
D. Iliadis, B. De Baets, T. Pahikkala and W. Waegeman
(2024) BMC BIOINFORMATICS. 25, 59.

(84) DeepMTP: A Python-based deep learning framework for multi-target prediction
D.Iliadis, B. De Baets and W. Waegeman
(2023) SOFTWAREX. 23, 101516.

(83) Valid prediction intervals for regression problems
N. Dewolf, B. De Baets and W. Waegeman
(2023) ARTIFICIAL INTELLIGENCE REVIEW. 56, 577-613.

(82) Multi-target prediction for dummies using two-branch neural networks
D. Iliadis, B. De Baets and W. Waegeman
(2022) MACHINE LEARNING. 111, 651-684.

(81) CpG Transformer for imputation of single-cell methylomes
G. De Waele, J. Clauwaert, G. Menschaert and W. Waegeman
(2022) BIOINFORMATICS. 38, 597-603.

(80) Novel transformer networks for improved sequence labeling in genomics
J. Clauwaert and W. Waegeman
(2022) IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 19, 97-106.

(79) Heterogeneity hampers the identification of general pressure injury risk factors in intensive care populations: A predictive modelling analysis
M. Deschepper, S. O. Labeau, W. Waegeman and S. I. Blot
(2022) INTENSIVE AND CRITICAL CARE NURSING. 68, 103117.

(78) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection
L. Van den Bulcke, A. De Backer, B. Ampe, S. Maes, J. Wittoeck, W. Waegeman, K. Hostens and S. Derycke
(2021) METABARCODING AND METAGENOMICS. 5, 233-247.

(77) Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: a large-scale benchmarking study
T. Mortier, A. Wieme, P. Vandamme and W. Waegeman
(2021) COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. 19, 6157-6168.

(76) Improving the performance of machine learning models for biotechnology: the quest for deux ex machina
F. Mey, J. Clauwaert, K. Van Huffel, W. Waegeman and M. De Mey
(2021) BIOTECHNOLOGY ADVANCES. 53, 107858.

(75) Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors
M. Deschepper, S. Labeau, W. Waegeman and S. Blot
(2021) INTENSIVE AND CRITICAL CARE NURSING. 65, 103033.

(74) Explainability in transformer models for functional genomics
J. Clauwaert, G. Menschaert and W. Waegeman
(2021) BRIEFINGS IN BIOINFORMATICS. 22, 1-11.

(73) Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting
J. Heyse, F. Schattenberg, P. Rubbens, S. Müller, W. Waegeman, N. Boon and R. Props
(2021) MSYSTEMS. 6, e00551-21.

(72) Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning
D. Hanssens, E. Van De Vijver, W. Waegeman, M. E. Everett, I. Moffat, A. Sarris and P. De Smedt
(2021) NEAR SURFACE GEOPHYSICS . 19, 541-556.

(71) Efficient set-valued prediction in multi-class classification
T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier and W. Waegeman
(2021) DATA MINING AND KNOWLEDGE DISCOVERY. 35, 1435-1469.

(70) Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
E. Hüllermeier and W. Waegeman
(2021) MACHINE LEARNING. 110, 457-506.

(69) PhenoGMM: Gaussian mixture modelling of cytometry data quantifies changes in microbial community structure
P. Rubbens, R. Props, F.-M. Kerckhof, N. Boon and W. Waegeman
(2021) MSPHERE. 6, e00530-20.

(68) High-resolution surveying with small-loop frequency domain electromagnetic systems: Efficient survey design and adaptive processing
D. Hanssens, W. Waegeman, Y. Declercq, H. Dierckx, H. Verschelde and P. De Smedt
(2021) IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. 9, 167-183.

(67) Cytometric fingerprints of gut microbiota predict Crohn's disease state
P. Rubbens, R. Props, F.-M. Kerckhof, N. Boon and W. Waegeman
(2021) THE ISME JOURNAL. 15, 354-358.

(66) Predictive design of sigma factor-specific promoters
M. Van Brempt, J. Clauwaert, F. Mey, M. Stock, J. Maertens, W. Waegeman and M. De Mey
(2020) NATURE COMMUNICATIONS. 11, 5822.

(65) Using structured pathology data to predict hospital-wide mortality at admission
M. Deschepper, W. Waegeman, D. Vogelaers and K. Eeckloo
(2020) PLOS ONE. 15, e0235117.

(64) Discriminating bacterial phenotypes at the population and single-cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting
C. García-Timermans, P. Rubbens, J. Heyse, F.-M. Kerckhof, R. Props, A. G. Skirtach, W. Waegeman and N. Boon
(2020) CYTOMETRY: PART A. 97, 713-726.

(63) Fast pathogen identification using single-cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight mass spectrometry data and deep learning methods
C. Papagiannopoulou, R. Parchen, P. Rubbens and W. Waegeman
(2020) ANALYTICAL CHEMISTRY. 92, 7523-7531.

(62) Algebraic shortcuts for leave-one-out cross-validation in supervised network inference
M. Stock, T. Pahikkala, A. Airola, W. Waegeman and B. De Baets
(2020) BRIEFINGS IN BIOINFORMATICS. 21, 262-271.

(61) Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry
P. Rubbens, M. Schmidt, R. Props, B. Biddanda, N. Boon, W. Waegeman and V. Denef
(2019) MSYSTEMS. 4, 00093-19.

(60) A protocol for automated timber species identification using metabolome profiling
V. Deklerck, T. Mortier, N. Goeders, R.B. Cody, W. Waegeman, E. Espinoza, J. Van Acker, J. Van den Bulcke and H. Beeckman
(2019) WOOD SCIENCE AND TECHNOLOGY. 53, 953-965.

(59) A hospital wide predictive model for unplanned readmission using hierarchical ICD data
M. Deschepper, K. Eeckloo, D. Vogelaers and W. Waegeman
(2019) COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. 173, 177-183.

(58) Learning single-cell distances from cytometry data
B. Nguyen, P. Rubbens, F.-M. Kerckhof, N. Boon, B. De Baets and W. Waegeman
(2019) CYTOMETRY PART A. 95, 782-791.

(57) Coculturing bacteria leads to reduced phenotypic heterogeneities
J. Heyse, B. Buysschaert, R. Props, P. Rubbens, A. Skirtach, W. Waegeman and N. Boon
(2019) APPLIED AND ENVIRONMENTAL MICROBIOLOGY. 85, e02814-18.

(56) DeepRibo: a neural network for the precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns
J. Clauwaert, G. Menschaert and W. Waegeman
(2019) NUCLEIC ACIDS RESEARCH. 47, e36.

(55) Multi-target prediction: a unifying view on problems and methods
W. Waegeman, K. Dembczyński and E. Hüllermeier
(2019) DATA MINING AND KNOWLEDGE DISCOVERY. 33, 293-324.

(54) Terrestrial evaporation response to modes of climate variability
B. Martens, W. Waegeman, W.A. Dorigo, N.E.C. Verhoest and D.G. Miralles
(2018) NPJ CLIMATE AND ATMOSPHERIC SCIENCE. 1, 43.

(53) Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data
R. Props, P. Rubbens, M. Besmer, B. Buysschaert, J. Sigrist, H. Weilenmann, W. Waegeman, N. Boon and F. Hammes
(2018) WATER RESEARCH. 145, 73-82.

(52) Global hydro-climatic biomes identified via multi-task learning
C. Papagiannopoulou, D.G. Miralles, M. Demuzere, N.E.C Verhoest and W. Waegeman
(2018) GEOSCIENTIFIC MODEL DEVELOPMENT. 11, 4139-4153.

(51) Effects of chlorhexidine gluconate oral care on hospital mortality: A hospital-wide, observational cohort study
M. Deschepper, W. Waegeman, K. Eeckloo, D. Vogelaers and S. Blot
(2018) INTENSIVE CARE MEDICINE. 44, 1017-1026.

(50) Label-free Raman characterization of bacteria calls for standardized procedures
C. García-Timermans, P. Rubbens, F.-M. Kerckhof, B. Buysschaert, D. Khalenkow, W. Waegeman, A. Skirtach and N. Boon
(2018) JOURNAL OF MICROBIOLOGICAL METHODS. 151, 69-75.

(49) A comparative study of pairwise learning methods based on Kernel Ridge Regression
M. Stock, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman
(2018) NEURAL COMPUTATION. 30, 2245-2283.

(48) Interpretation and visualisation of data in dairy herds
K. Hermans, G. Opsomer, W. Waegeman, S. Moerman, J. De Koster, M. Van Eetvelde, B. Van Ranst and M. Hostens
(2018) IN PRACTICE. 40, 195-203.

(47) Stripping flow cytometry: how many detectors do we need for bacterial identification?
P. Rubbens, R. Props, C. Garcia-Timmermans, N. Boon and W. Waegeman
(2017) CYTOMETRY PART A. 91, 1184-1191.

(46) Vegetation anomalies caused by antecedent precipitation in most of the world
C. Papagiannopoulou, D.G. Miralles, W.A. Dorigo, N.E.C. Verhoest, M. Depoorter and W. Waegeman
(2017) ENVIRONMENTAL RESEARCH LETTERS . 12, 074016.

(45) Novel approaches to assess the quality of fertility data stored in dairy herd management software
K. Hermans, W. Waegeman, G. Opsomer, B. Van Ranst, J. De Koster, M. Van Eetvelde and M. Hostens
(2017) JOURNAL OF DAIRY SCIENCE. 100, 4078-4089.

(44) Potentials and limitations of existing forecasting models for Alternaria on potatoes: challenges for model improvement
S. Landschoot, J. De Reu, K. Audenaert, P. Vanhaverbeke, G. Haesaert, B. De Baets and W. Waegeman
(2017) POTATO RESEARCH. 60, 61-76.

(43) A non-linear Granger causality framework to investigate climate-vegetation dynamics
C. Papagiannopoulou, D.G. Miralles, S. Decubber, M. Demuzere, N.E.C. Verhoest, W.A. Dorigo and W. Waegeman
(2017) GEOSCIENTIFIC MODEL DEVELOPMENT. 10, 1945-1960.

(42) miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure
G. Van Peer, A. De Paepe, M. Stock, J. Anckaert, P-J. Volders, J. Vandesompele, B. De Baets and W. Waegeman
(2017) NUCLEIC ACIDS RESEARCH. 45, e51.

(41) Linear filtering reveals false negatives in species interaction data
M. Stock, T. Poisot, W. Waegeman and B. De Baets
(2017) SCIENTIFIC REPORTS. 7, 45908.

(40) Flow cytometric single-cell identification of populations in synthetic bacterial communities
P. Rubbens, R. Props, N. Boon and W. Waegeman
(2017) PLOS ONE. 12, e0169754.

(39) Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils
P. Nguyen, A. Haghverdi, J. de Pue, Y-D. Botula, K. Le, W. Waegeman and W. Cornelis
(2017) BIOSYSTEMS ENGINEERING. 153C, 12-27.

(38) Absolute quantification of microbial taxon abundances
R. Props, F-M. Kerckhof, P. Rubbens, J. De Vrieze, E. Hernandez Sanabria, W. Waegeman, P. Monsieurs, F. Hammes and N. Boon
(2017) THE ISME JOURNAL. 11, 584-587.

(37) Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
M. Stock, K. Dembczyński, B. De Baets and W. Waegeman
(2016) DATA MINING AND KNOWLEDGE DISCOVERY. 30, 1370-1394.

(36) Data-driven recipe completion using machine learning methods
M. De Clercq, M. Stock, B. De Baets and W. Waegeman
(2016) TRENDS IN FOOD SCIENCE & TECHNOLOGY. 49, 1-13.

(35) Prediction of subacute ruminal acidosis based on milk fatty acids: a comparison of linear discriminant analysis and support vector machine approaches for model development
E. Colman, W. Waegeman, B. De Baets and V. Fievez
(2015) COMPUTERS AND ELECTRONICS IN AGRICULTURE. 111, 179-185.

(34) A community effort to assess and improve drug sensitivity prediction algorithms
J.C. Costello, L.M. Heiser, E. Georgii, M. Gönen, M.P. Menden, N.J. Wang, M. Bansal, M. Ammad-ud-din, P. Hintsanen, S.A. Khan, J.-P. Mpindi, O. Kallioniemi, A. Honkela, T. Aittokallio, K. Wennerberg, J.-P. Abbuehl, J. Allen, R.B. Altman, S. Balcome, A. Battle, A. Bender, B. Berger, J. Bernard, M. Bhattacharjee, K. Bhuvaneshwar, A.A. Bieberich, F. Boehm, A. Califano, C. Chan, B. Chen, T.-H. Chen, J. Choi, L.P. Coelho, T. Cokelaer, C.J. Creighton, J. Cui, W. Dampier, V.J. Davisson, B. De Baets, R. Deshpande, B. DiCamillo, M. Dundar, Z. Duren, A. Ertel, H. Fan, H. Fang, R. Gauba, A. Gottlieb, M. Grau, Y. Gusev, M.J. Ha, L. Han, M. Harris, N. Henderson, H.A. Hejase, K. Homicsko, J.P. Hou, W. Hwang, A.P. Ijzerman, B. Karacali, S. Keles, C. Kendziorski, J. Kim, M. Kim, Y. Kim, D.A. Knowles, D. Koller, J. Lee, J.K. Lee, E.B. Lenselink, B. Li, B. Li, J. Li, H. Liang, J. Ma, S. Madhavan, S. Mooney, C.L. Myers, M.A Newton, J.P. Overington, R. Pal, J. Peng, R. Pestell, R.J. Prill, P. Qiu, B. Rajwa, A. Sadanandam, F. Sambo, H. Shin, J. Song, L. Song, A. Sridhar, M. Stock, W. Sun, T. Ta, M. Tadesse, M. Tan, H. Tang, D. Theodorescu, G.M. Toffolo, A. Tozeren, W. Trepicchio, N. Varoquaux, J.-P. Vert, W. Waegeman, T. Walter, Q. Wan, D. Wang, W. Wang, Y. Wang, Z. Wang, J.K. Wegner, T. Wu, T. Xia, G. Xiao, Y. Xie, Y. Xu, J. Yang, Y. Yuan, S. Zhang, X.-S. Zhang, J. Zhao, C. Zuo, H.W.T. van Vlijmen, G.J.P. van Westen, J.J. Collins, D. Gallahan, D. Singer, J. Saez-Rodriguez, S. Kaski, J.W. Gray and G. Stolovitzky
(2014) NATURE BIOTECHNOLOGY. 32, 1202-1212.

(33) On the Bayes-optimality of F-measure maximizers
W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng and E. Hüllermeier
(2014) JOURNAL OF MACHINE LEARNING RESEARCH. 15, 3333-3388.

(32) Identification of functionally-related enzymes by learning-to-rank methods and cavity-based similarity measures
M. Stock, T. Fober, E. Hüllermeier, S. Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman
(2014) IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 11, 1157-1169.

(31) Predicting spatio-temporal Culicoides imicola distributions based on environmental habitat characteristics and species dispersal
J. Peters, W. Waegeman, J. Van doninck, E. Ducheyne, C. Calvete, J. Lucientes, N.E.C. Verhoest and B. De Baets
(2014) ECOLOGICAL INFORMATICS. 22, 69-80.

(30) Exploration and prediction of interactions between methanotrophs and heterotrophs
M. Stock, S. Hoefman, F.-M. Kerckhof, N. Boon, P. De Vos, B. De Baets, K. Heylen and W. Waegeman
(2013) RESEARCH IN MICROBIOLOGY. 10, 1045-1054.

(29) Ordinal regression models for predicting deoxynivalenol in winter wheat
S. Landschoot, W. Waegeman, K. Audenaert, G. Haesaert and B. De Baets
(2013) PLANT PATHOLOGY. 62, 1319-1329.

(28) Efficient regularized least-squares algorithms for conditional ranking on relational data
T. Pahikkala, A. Airola, M. Stock, B. De Baets and W. Waegeman
(2013) MACHINE LEARNING. 93, 321-356.

(27) Combined exposure of cyanobacteria and carbaryl results in antagonistic effects on the reproduction of Daphnia pulex
J. Asselman, J. Meys, W. Waegeman, B. De Baets and K.A.C. De Schamphelaere
(2013) ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. 32, 2153-2158.

(26) Influence of maize-wheat rotation systems on Fusarium head blight infection and deoxynivalenol content in wheat under low versus high disease pressure
S. Landschoot, K. Audenaert, W. Waegeman, B. De Baets and G. Haesaert
(2013) CROP PROTECTION. 52, 14-21.

(25) Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models
S. Fukuda, B. De Baets, W. Waegeman, J. Verwaeren and A.M. Mouton
(2013) ENVIRONMENTAL MODELLING AND SOFTWARE. 447, 1-6.

(24) A field-specific web tool for the prediction of Fusarium head blight and DON content in Belgium
S. Landschoot, W. Waegeman, K. Audenaert, P. Van Damme, J. Vandepitte, B. De Baets and G. Haesaert
(2013) COMPUTERS AND ELECTRONICS IN AGRICULTURE. 93, 140-148.

(23) 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.

(22) 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.

(21) 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.

(20) 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.

(19) 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.

(18) 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.

(17) Supervised learning algorithms for multi-class classification problems with partial class memberships
W. Waegeman, J. Verwaeren, B. Slabbinck and B. De Baets
(2011) FUZZY SETS AND SYSTEMS. 184, 106-125.

(16) Connection between primary Fusarium inoculum on gramineous weeds, crop residues and soil samples and the final population on wheat ears in Flanders
S. Landschoot, K. Audenaert, W. Waegeman, B. Pycke, B. Bekaert, B. De Baets and G. Haesaert
(2011) CROP PROTECTION. 30, 1297-1305.

(15) Effect of model formulation on the optimization of a genetic Takagi-Sugeno fuzzy system for fish habitat suitability evaluation
S. Fukuda, B. De Baets, A.M. Mouton, W. Waegeman, J. Nakajima, T. Mukai, K. Hiramatsu and N. Onikura
(2011) ECOLOGICAL MODELLING. 222, 1401-1413.

(14) On the ERA representability of pairwise bipartite ranking functions
W. Waegeman and B. De Baets
(2011) ARTIFICIAL INTELLIGENCE JOURNAL. 175, 1223-1250.

(13) Bacterial species identification from MALDI-TOF spectra through data analysis and machine learning
K. De Bruyne, B. Slabbinck, W. Waegeman, P. Vauterin, B. De Baets and P. Vandamme
(2011) SYSTEMATIC AND APPLIED MICROBIOLOGY. 34, 20-29.

(12) An experimental comparison of cross-validation techniques for estimating the area under the ROC curve
A. Airola, T. Pahikkala, W. Waegeman, B. De Baets and T. Salakoski
(2011) COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 55, 1828-1844.

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

(10) 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.

(9) 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.

(8) 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.

(7) Kernel-based learning methods for preference aggregation
W. Waegeman, B. De Baets and L. Boullart
(2009) 4OR. 7, 169-189.

(6) Learning to Rank: a ROC-based Graph-Theoretic Approach
W. Waegeman
(2009) 4OR. 7, 399-402.

(5) Learning layered ranking functions with structured Support Vector Machines
W. Waegeman, B. De Baets and L. Boullart
(2008) NEURAL NETWORKS. 21, 1511-1523.

(4) Classifying carpets based on laser scanner data
W. Waegeman, J. Cottyn, B. Wyns, L. Boullart, B. De Baets, L. Van Langenhove and J. Detand
(2008) ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. 21, 907-918.

(3) On the scalability of ordered multi-class ROC analysis
W. Waegeman, B. De Baets and L. Boullart
(2008) COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 52, 3371-3388.

(2) ROC analysis in ordinal regression learning
W. Waegeman, B. De Baets and L. Boullart
(2008) PATTERN RECOGNITION LETTERS. 29, 1-9.

(1) Polymorphisms in the Ficolin 1 gene (FCN1) are associated with susceptibility to the development of rheumatoid arthritis
B. Vander Cruyssen, L. Nuytinck, L. Boullart, D. Elewaut, W. Waegeman, M. Van Thielen, E. De Meester, K. Lebeer, R. Rossau and F. De Keyser
(2007) RHEUMATOLOGY. 46, 1792-1795.

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)

(38) Set-valued prediction in hierarchical classification with constrained representation complexity
T. Mortier, E. Hüllermeier, K. Dembczyński and W. Waegeman
(2022) INTERNATIONAL CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE.
Eindhoven, The Netherlands, 11. .

(37) Investigating time series classification techniques for rapid pathogen identification with single-cell MALDI-TOF mass spectrum data
C. Papagiannopoulou, E. Parchen and W. Waegeman
(2019) JOINT EUROPEAN CONFERENCE ON MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, 2019.
Würzburg, Germany, 15. .

(36) Deep F-Measure Maximization in Multi-label Classification: A Comparative Study
S. De Cubber, T. Mortier, K. Dembczyński and W. Waegeman
(2018) JOINT EUROPEAN CONFERENCE ON MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES.
Dublin, Ireland, 15. .

(35) Analyzing Granger causality in climate data with time series classification methods
C. Papagiannopoulou, S. Decubber, D. Miralles, M. Demuzere, N. Verhoest and W. Waegeman
(2017) JOINT EUROPEAN CONFERENCE ON MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES.
Skopje, Macedonia, 12 pages. .

(34) Consistency of Probabilistic Classifier Trees
K. Dembczyński, W. Kotłowski, W. Waegeman, R. Busa-Fekete, E. Hüllermeier
(2016) JOINT EUROPEAN CONFERENCE ON MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES.
Riva del Garda, Italy, 16 pages. .

(33) Discovering relationships in climate-vegetation dynamics using satellite data
C. Papagiannopoulou, D. Miralles, M. Depoorter, N. Verhoest, W. Dorigo, W. Waegeman
(2016) PROCEEDINGS OF AALTD 2016: SECOND ECML/PKDD INTERNATIONAL WORKSHOP ON ADVANCED ANALYTICS AND LEARNING ON TEMPORAL DATA.
Riva del Garda, Italy, 8 pages. .

(32) Recipe Completion using Machine Learning Techniques
M. De Clercq, M. Stock, B. De Baets and W. Waegeman
(2015) ICML 2015 WORKSHOP ON CONSTRUCTIVE MACHINE LEARNING.
Lille, France, 4 pages. .

(31) A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
T. Pahikkala, M. Stock, A. Airola, T. Aittokallio, B. De Baets and W. Waegeman
(2014) MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES.
Nancy, France, 517-532. .

(30) Plug-in-rule vs. structured loss minimization in multi-label classification
K. Dembczynski, V. Kotlowski, A. Jachnik, W. Waegeman and E. Hüllermeier
(2013) INTERNATIONAL CONFERENCE ON MACHINE LEARNING.
Atlanta, USA, 8 pages. .

(29) 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. .

(28) 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. .

(27) 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.

(26) F-Measure Maximization in Multilabel Classification
W. Cheng, K. Dembczynski, E. Hüllermeier, A. Jaroszewicz and W. Waegeman
(2012) JRC DATA MINING CONTEST.
, 8 pages. .

(25) An exact algorithm for F-measure maximization
K. Dembczynski, W. Waegeman , W. Cheng and E. Hüllermeier
(2011) ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 24.
Granada, Spain, 815-824. .

(24) Joint mode estimation in multi-label classification by chaining
K. Dembczynski, W. Waegeman and E. Hüllermeier
(2011) ECML WORKSHOP ON COLLECTIVE INFERENCE AND LEARNING ON STRUCTURED DATA.
Athens, Greece, 12 pages. .

(23) ERA Ranking Representability: the missing link between ordinal regression and multi-class classification
W. Waegeman and B. De Baets
(2011) SPECIAL SESSION ON ORDINAL REGRESSION.
Cordoba, Spain, 1188-1193. Proc. of the International Conference on Intelligent Systems Design and Applications.

(22) Learning Valued Relations from Data
W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, B. De Baets
(2011) EUROFUSE 2011 WORKSHOP ON FUZZY METHODS FOR KNOWLEDGE-BASED SYSTEMS.
Douro, Portugal, 257-268. Advances in Intelligent and Soft Computing 107.

(21) Modelling Fish Habitat Preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparison
S. Fukuda, W. Waegeman, A. Mouton and B. De Baets
(2011) EUROFUSE 2011 WORKSHOP ON FUZZY METHODS FOR KNOWLEDGE-BASED SYSTEMS.
Douro, Portugal, 257-268. Advances in Intelligent and Soft Computing 107.

(20) A Discussion on The Accuracy-Interpretability Tradeoff in Modelling Fish Habitat Preference Using A Genetic Takagi-Sugeno Fuzzy System
S. Fukuda, N. Onikura, B. De Baets, W. Waegeman, A. Mouton, J. Nakajima, T. Mukai
(2011) IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE.
Paris, France, 81-86. .

(19) 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. .

(18) 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. .

(17) 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.

(16) 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.

(15) 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. .

(14) 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. .

(13) 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. .

(12) 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. .

(11) Learning Partial Class Memberships in Multi-Class Classification Problems: a Probabilistic Approach
W. Waegeman and B. De Baets
(2009) EUROFUSE WORKSHOP ON PREFERENCE HANDLING AND DECISION SUPPORT.
Pamplona, Spain, 6 pages. .

(10) A Comparison of AUC Estimators in Small-Sample Studies
A. Airola, T. Pahikkala, W. Waegeman, B. De Baets and T. Salakoski
(2009) 3RD WORKSHOP ON MACHINE LEARNING IN SYSTEMS BIOLOGY.
Ljubljana, Slovenia, 3-13. MLR Workshop and Conference Proceedings 8.

(9) From Ranking to Intransitive Preference Learning: Rock-Paper-Scissors and Beyond
T. Pahikkala, W. Waegeman, E. Tsivtsivadze, T. Salakoski and B. De Baets
(2009) ECML 2009 WORKSHOP ON PREFERENCE LEARNING.
Bled, Slovenia, 16 pages. .

(8) Learning Intransitive Reciprocal Relations with Regularized Least-Squares
T. Pahikkala, W. Waegeman, E. Tsivtsivadze, T. Salakoski and B. De Baets
(2009) BENELEARN 2008 BENELUX CONFERENCE ON MACHINE LEARNING.
Tilburg, The Netherlands, 8 pages. .

(7) Integrating Expert Knowledge into Kernel-based Preference Models
W. Waegeman, B. De Baets and L. Boullart
(2008) ECML WORKSHOP ON PREFERENCE LEARNING.
Antwerp, Belgium, 14 pages. .

(6) A Graph-theoretic Approach for Reducing One-versus-one Multi-class Classification to Ranking
W. Waegeman, B. De Baets and L. Boullart
(2008) INTERNATIONAL WORKSHOP ON MINING AND LEARNING WITH GRAPHS.
Helsinki, Finland, 3 pages. .

(5) Learning a Layered Graph with a Maximal Number of Paths Connecting Source and Sink
W. Waegeman, B. De Baets and L. Boullart
(2007) ICML WORKSHOP ON CONSTRAINED OPTIMIZATION AND STRUCTURED OUTPUT SPACES.
Corvallis, OR, USA, 6 pages. .

(4) Machine Learning Algorithms for Carpets Classification
R. Damian, R. De Keyser, L. Boullart, W. Waegeman, L. Van Langenhove and C. Lazar
(2007) INTERNATIONAL SYMPOSIUM ON AUTOMATIC CONTROL AND COMPUTER SCIENCE.
Iasi, Romania, 6 pages. .

(3) A Comparison of Different ROC Measures for Ordinal Regression
W. Waegeman, B. De Baets and L. Boullart
(2006) ICML WORKSHOP ON ROC ANALYSIS IN MACHINE LEARNING.
Pittsburgh, PA, USA, 7 pages. .

(2) An Ensemble of Weighted Support Vector Machines for Ordinal Regression
W. Waegeman and L. Boullart
(2006) INTERNATIONAL CONFERENCE ON NEURAL NETWORKS.
Vienna, 5 pages. .

(1) On the calibration of probabilistic classifier sets
T. Mortier, V. Bengs, E. Hüllermeier, S. Luca and W. Waegeman
(2023) 26TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTCS (AISTATS) 2023.
Valencia, Spain, . .

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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