Waegeman Willem

willem.waegeman@ugent.be
@KERMIT, office 110.069
(+32) 9 264.92.00

linkedin logo Waegeman Willem
ORCID logo https://orcid.org/0000-0002-5950-3003
Biblio logoBiblio UGent
Google Scholar logoGoogle Scholar

Research Interests: Machine learning

Journal papers Conference papers

Journal papers

(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(14) On the ERA representability of pairwise bipartite ranking functions
W. Waegeman and B. De Baets
(2011) ARTIFICIAL INTELLIGENCE JOURNAL. 175, 1223-1250.
Biblio logo(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.
Biblio logo(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.
Biblio logo(11) A transitivity analysis of bipartite rankings in pairwise multi-class classification
W. Waegeman and B. De Baets
(2010) INFORMATION SCIENCES. 180, 4099-4117.
Biblio logo(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.
Biblio logo(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.
Biblio logo(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.
Biblio logo(7) Kernel-based learning methods for preference aggregation
W. Waegeman, B. De Baets and L. Boullart
(2009) 4OR. 7, 169-189.
Biblio logo(6) Learning to Rank: a ROC-based Graph-Theoretic Approach
W. Waegeman
(2009) 4OR. 7, 399-402.
Biblio logo(5) Learning layered ranking functions with structured Support Vector Machines
W. Waegeman, B. De Baets and L. Boullart
(2008) NEURAL NETWORKS. 21, 1511-1523.
Biblio logo(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.
Biblio logo(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.
Biblio logo(2) ROC analysis in ordinal regression learning
W. Waegeman, B. De Baets and L. Boullart
(2008) PATTERN RECOGNITION LETTERS. 29, 1-9.
Biblio logo(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

(34) 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.
(33) 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.
(32) 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.
(31) 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.
(30) 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.
(29) 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.
(28) 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.
(27) 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.
(26) 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.
(25) F-Measure Maximization in Multilabel Classification
W. Cheng, K. Dembczynski, E. Hüllermeier, A. Jaroszewicz and W. Waegeman
(2012) JRC DATA MINING CONTEST.
, 8 pages.
(24) 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.
(23) 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.
(22) 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.
(21) 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.
(20) 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.
(19) 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.
(18) 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.
(17) 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.
(16) 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.
(15) 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.
(14) 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.
(13) 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.
(12) 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.
(11) 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.
(10) 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.
(9) 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.
(8) 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.
(7) 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.
(6) 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.
(5) 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.
(4) 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.
(3) 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.
(2) 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.
(1) An Ensemble of Weighted Support Vector Machines for Ordinal Regression
W. Waegeman and L. Boullart
(2006) INTERNATIONAL CONFERENCE ON NEURAL NETWORKS.
Vienna, 5 pages.