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Celine Vens
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2020 – today
- 2024
- [j38]Michela Venturini
, Fateme Nateghi Haredasht
, Frantisek Sabovcik, Robert J. H. Miller
, Tatiana Kuznetsova, Celine Vens
:
Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches. IEEE Access 12: 89754-89762 (2024) - [j37]Michela Venturini
, Ingrid Van Keilegom
, Wouter De Corte, Celine Vens:
Predicting time-to-intubation after critical care admission using machine learning and cured fraction information. Artif. Intell. Medicine 150: 102817 (2024) - [j36]Felipe Kenji Nakano
, Karolijn Dulfer, Ilse Vanhorebeek
, Pieter J. Wouters, Sascha C. Verbruggen, Koen F. Joosten, Fabian Güiza Grandas
, Celine Vens
, Greet Van den Berghe
:
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission. Comput. Methods Programs Biomed. 250: 108166 (2024) - [j35]Jasper de Boer
, Klest Dedja
, Celine Vens:
SurvivalLVQ: Interpretable supervised clustering and prediction in survival analysis via Learning Vector Quantization. Pattern Recognit. 153: 110497 (2024) - 2023
- [j34]Klest Dedja
, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens
:
BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor. IEEE Access 11: 41348-41367 (2023) - [j33]Fateme Nateghi Haredasht
, Kazeem Adesina Dauda
, Celine Vens
:
Exploiting Censored Information in Self-Training for Time-to-Event Prediction. IEEE Access 11: 96831-96840 (2023) - [j32]Miguel Romero
, Felipe Kenji Nakano, Jorge Finke
, Camilo Rocha
, Celine Vens:
Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification. Comput. Biol. Medicine 152: 106423 (2023) - [j31]Alireza Gharahighehi
, Celine Vens:
Diversification in session-based news recommender systems. Pers. Ubiquitous Comput. 27(1): 5-15 (2023) - [j30]Saulo Martiello Mastelini
, Felipe Kenji Nakano, Celine Vens
, André Carlos Ponce de Leon Ferreira de Carvalho
:
Online Extra Trees Regressor. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6755-6767 (2023) - [c25]Robbe D'hondt
, Sinead Moylett
, An Goris
, Celine Vens
:
A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis. AIME 2023: 25-34 - [c24]Klest Dedja, Felipe Kenji Nakano, Celine Vens:
Active Learning for Survival Analysis with Incrementally Disclosed Label Information. IAL@PKDD/ECML 2023: 46-64 - [c23]Alireza Gharahighehi
, Michela Venturini
, Achilleas Ghinis
, Frederik Cornillie
, Celine Vens
:
Extending Bayesian Personalized Ranking with Survival Analysis for MOOC Recommendation. UMAP (Adjunct Publication) 2023: 56-59 - [e3]Toon Calders
, Celine Vens
, Jefrey Lijffijt
, Bart Goethals
:
Artificial Intelligence and Machine Learning - 34th Joint Benelux Conference, BNAIC/Benelearn 2022, Mechelen, Belgium, November 7-9, 2022, Revised Selected Papers. Communications in Computer and Information Science 1805, Springer 2023, ISBN 978-3-031-39143-9 [contents] - [i9]Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos:
HypeRS: Building a Hypergraph-driven ensemble Recommender System. CoRR abs/2306.12800 (2023) - 2022
- [j29]Alireza Gharahighehi
, Konstantinos Pliakos
, Celine Vens
:
Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction. IEEE Access 10: 117189-117198 (2022) - [j28]Bin Liu
, Konstantinos Pliakos, Celine Vens, Grigorios Tsoumakas:
Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery. Appl. Intell. 52(4): 3705-3727 (2022) - [j27]Bijit Roy
, Tomaz Stepisnik, The Pooled Resource Open-Access A. L. S. Clinical Trials Consortium, Celine Vens, Saso Dzeroski
:
Survival analysis with semi-supervised predictive clustering trees. Comput. Biol. Medicine 141: 105001 (2022) - [j26]Fateme Nateghi Haredasht
, Celine Vens:
Predicting Survival Outcomes in the Presence of Unlabeled Data. Mach. Learn. 111(11): 4139-4157 (2022) - [j25]Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Deep tree-ensembles for multi-output prediction. Pattern Recognit. 121: 108211 (2022) - [c22]Michela Venturini
, Ingrid Van Keilegom
, Wouter De Corte
, Celine Vens
:
A Novel Survival Analysis Approach to Predict the Need for Intubation in Intensive Care Units. AIME 2022: 358-364 - [c21]Alireza Gharahighehi
, Felipe Kenji Nakano, Celine Vens:
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering. IntelliSys (1) 2022: 531-545 - [i8]Alireza Gharahighehi
, Felipe Kenji Nakano, Celine Vens:
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering. CoRR abs/2203.05954 (2022) - [i7]Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Explaining random forest prediction through diverse rulesets. CoRR abs/2203.15511 (2022) - [i6]Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens:
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification. CoRR abs/2207.06237 (2022) - [i5]Fateme Nateghi Haredasht, Celine Vens:
Predicting Survival Outcomes in the Presence of Unlabeled Data. CoRR abs/2210.13891 (2022) - 2021
- [j24]Alireza Gharahighehi
, Celine Vens, Konstantinos Pliakos:
Fair multi-stakeholder news recommender system with hypergraph ranking. Inf. Process. Manag. 58(5): 102663 (2021) - [j23]Márcio P. Basgalupp
, Ricardo Cerri, Leander Schietgat
, Isaac Triguero
, Celine Vens:
Beyond global and local multi-target learning. Inf. Sci. 579: 508-524 (2021) - [j22]Alireza Gharahighehi
, Celine Vens:
Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems. SN Comput. Sci. 2(1): 39 (2021) - [j21]Konstantinos Pliakos
, Celine Vens
, Grigorios Tsoumakas
:
Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. IEEE ACM Trans. Comput. Biol. Bioinform. 18(4): 1596-1607 (2021) - [c20]Alireza Gharahighehi
, Celine Vens, Konstantinos Pliakos:
An Ensemble Hypergraph Learning Framework for Recommendation. DS 2021: 295-304 - [c19]Klest Dedja
, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Explaining a Random Survival Forest by Extracting Prototype Rules. PKDD/ECML Workshops (2) 2021: 451-458 - [i4]Alireza Gharahighehi, Celine Vens:
Diversification in Session-based News Recommender Systems. CoRR abs/2102.03265 (2021) - 2020
- [j20]Konstantinos Pliakos
, Celine Vens:
Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. BMC Bioinform. 21(1): 49 (2020) - [j19]Felipe Kenji Nakano
, Ricardo Cerri, Celine Vens:
Active learning for hierarchical multi-label classification. Data Min. Knowl. Discov. 34(5): 1496-1530 (2020) - [j18]Len Feremans, Boris Cule, Celine Vens, Bart Goethals
:
Combining instance and feature neighbours for extreme multi-label classification. Int. J. Data Sci. Anal. 10(3): 215-231 (2020) - [c18]Alireza Gharahighehi
, Celine Vens, Konstantinos Pliakos:
Multi-stakeholder News Recommendation Using Hypergraph Learning. PKDD/ECML Workshops 2020: 531-535 - [c17]Bruna Zamith Santos
, Felipe Kenji Nakano, Ricardo Cerri, Celine Vens:
Predictive Bi-clustering Trees for Hierarchical Multi-label Classification. ECML/PKDD (3) 2020: 701-718 - [c16]Alireza Gharahighehi, Celine Vens:
Making Session-based News Recommenders Diversity-aware. OHARS@RecSys 2020: 60-66 - [i3]Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Deep tree-ensembles for multi-output prediction. CoRR abs/2011.02829 (2020) - [i2]Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos:
Fair Multi-Stakeholder News Recommender System with Hypergraph ranking. CoRR abs/2012.00387 (2020) - [i1]Bin Liu, Konstantinos Pliakos, Celine Vens, Grigorios Tsoumakas:
Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery. CoRR abs/2012.12325 (2020)
2010 – 2019
- 2019
- [j17]Felipe Kenji Nakano, Mathias Lietaert, Celine Vens:
Machine learning for discovering missing or wrong protein function annotations - A comparison using updated benchmark datasets. BMC Bioinform. 20(1): 485:1-485:32 (2019) - [j16]Konstantinos Pliakos
, Celine Vens:
Network inference with ensembles of bi-clustering trees. BMC Bioinform. 20(1): 525:1-525:12 (2019) - [j15]Konstantinos Pliakos, Seang-Hwane Joo, Jung Yeon Park
, Frederik Cornillie
, Celine Vens, Wim van den Noortgate
:
Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Comput. Educ. 137: 91-103 (2019) - [c15]Alireza Gharahighehi, Celine Vens:
News Topic Recommendation Using an Extended Bayesian Personalized Ranking. BNAIC/BENELEARN 2019 - [c14]Alireza Gharahighehi
, Celine Vens:
Extended Bayesian Personalized Ranking Based on Consumption Behavior. BNAIC/BENELEARN (Selected Papers) 2019: 152-164 - 2018
- [j14]Konstantinos Pliakos
, Celine Vens:
Mining features for biomedical data using clustering tree ensembles. J. Biomed. Informatics 85: 40-48 (2018) - [j13]Konstantinos Pliakos
, Celine Vens:
Network representation with clustering tree features. J. Intell. Inf. Syst. 51(2): 341-365 (2018) - [j12]Konstantinos Pliakos
, Pierre Geurts, Celine Vens:
Global multi-output decision trees for interaction prediction. Mach. Learn. 107(8-10): 1257-1281 (2018) - [j11]Leander Schietgat, Celine Vens
, Ricardo Cerri
, Carlos N. Fischer
, Eduardo P. Costa, Jan Ramon, Claudia M. A. Carareto
, Hendrik Blockeel
:
A machine learning based framework to identify and classify long terminal repeat retrotransposons. PLoS Comput. Biol. 14(4) (2018) - [c13]Annelies Raes, Pieter Vanneste, Celine Vens, Fien Depaepe:
Teaching for Versus Through Problem Solving: Impact on Teaching and Learning. ICLS 2018 - 2017
- [c12]Len Feremans, Boris Cule, Celine Vens, Bart Goethals
:
Combining Instance and Feature Neighbors for Efficient Multi-label Classification. DSAA 2017: 109-118 - [e2]Michelangelo Ceci, Jaakko Hollmén, Ljupco Todorovski, Celine Vens, Saso Dzeroski:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10534, Springer 2017, ISBN 978-3-319-71248-2 [contents] - [e1]Michelangelo Ceci, Jaakko Hollmén, Ljupco Todorovski, Celine Vens, Saso Dzeroski:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II. Lecture Notes in Computer Science 10535, Springer 2017, ISBN 978-3-319-71245-1 [contents] - 2016
- [j10]Isaac Triguero
, Celine Vens:
Labelling strategies for hierarchical multi-label classification techniques. Pattern Recognit. 56: 170-183 (2016) - [c11]Konstantinos Pliakos, Celine Vens:
Feature Induction and Network Mining with Clustering Tree Ensembles. NFMCP@PKDD/ECML 2016: 3-18 - 2015
- [j9]Carlos N. Fischer
, Claudia M. A. Carareto
, Renato Augusto Corrêa dos Santos
, Ricardo Cerri
, Eduardo P. Costa, Leander Schietgat, Celine Vens:
Learning HMMs for nucleotide sequences from amino acid alignments. Bioinform. 31(11): 1836-1838 (2015) - 2014
- [c10]Celine Vens, Sofie Van Gassen, Tom Dhaene, Yvan Saeys
:
Complex Aggregates over Clusters of Elements. ILP 2014: 181-193 - 2013
- [j8]Dragi Kocev
, Celine Vens, Jan Struyf, Saso Dzeroski
:
Tree ensembles for predicting structured outputs. Pattern Recognit. 46(3): 817-833 (2013) - [c9]Pan Hu, Celine Vens, Bart Verstrynge, Hendrik Blockeel
:
Generalizing from Example Clusters. Discovery Science 2013: 64-78 - [c8]Celine Vens, Bart Verstrynge, Hendrik Blockeel
:
Semi-supervised Clustering with Example Clusters. KDIR/KMIS 2013: 45-51 - 2012
- [j7]Joris Maervoet, Celine Vens, Greet Vanden Berghe, Hendrik Blockeel
, Patrick De Causmaecker
:
Outlier detection in relational data: A case study in geographical information systems. Expert Syst. Appl. 39(5): 4718-4728 (2012) - 2011
- [j6]Celine Vens, Marie-Noëlle Rosso, Etienne G. J. Danchin
:
Identifying discriminative classification-based motifs in biological sequences. Bioinform. 27(9): 1231-1238 (2011) - [c7]Celine Vens, Fabrizio Costa
:
Random Forest Based Feature Induction. ICDM 2011: 744-753 - 2010
- [j5]Leander Schietgat, Celine Vens, Jan Struyf, Hendrik Blockeel
, Dragi Kocev
, Saso Dzeroski
:
Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinform. 11: 2 (2010) - [c6]Celine Vens, Eduardo P. Costa, Hendrik Blockeel
:
Top-Down Induction of Phylogenetic Trees. EvoBIO 2010: 62-73 - [p1]Celine Vens, Leander Schietgat, Jan Struyf, Hendrik Blockeel
, Dragi Kocev
, Saso Dzeroski
:
Predicting Gene Function using Predictive Clustering Trees. Inductive Databases and Constraint-Based Data Mining 2010: 365-387
2000 – 2009
- 2008
- [j4]Celine Vens:
Complex aggregates in relational learning. AI Commun. 21(2-3): 219-220 (2008) - [j3]Celine Vens, Jan Struyf, Leander Schietgat, Saso Dzeroski
, Hendrik Blockeel
:
Decision trees for hierarchical multi-label classification. Mach. Learn. 73(2): 185-214 (2008) - 2007
- [b1]Celine Vens:
Complex aggregates in relational learning ; Complexe aggregaten in relationeel leren. Katholieke Universiteit Leuven, Belgium, 2007 - [c5]Dragi Kocev
, Celine Vens, Jan Struyf, Saso Dzeroski
:
Ensembles of Multi-Objective Decision Trees. ECML 2007: 624-631 - [c4]Aneta Ivanovska, Celine Vens, Saso Dzeroski, Nathalie Colbach:
Studying the Presence of Genetically Modified Variants in Organic Oilseed Rape by Using Relational Data Mining. EnviroInfo (1) 2007: 417-424 - 2006
- [j2]Celine Vens, Hendrik Blockeel:
A simple regression based heuristic for learning model trees. Intell. Data Anal. 10(3): 215-236 (2006) - [j1]Anneleen Van Assche, Celine Vens, Hendrik Blockeel
, Saso Dzeroski
:
First order random forests: Learning relational classifiers with complex aggregates. Mach. Learn. 64(1-3): 149-182 (2006) - [c3]Celine Vens, Jan Ramon, Hendrik Blockeel
:
ReMauve: A Relational Model Tree Learner. ILP 2006: 424-438 - [c2]Celine Vens, Jan Ramon, Hendrik Blockeel
:
Refining Aggregate Conditions in Relational Learning. PKDD 2006: 383-394 - 2004
- [c1]Celine Vens, Anneleen Van Assche, Hendrik Blockeel, Saso Dzeroski
:
First Order Random Forests with Complex Aggregates. ILP 2004: 323-340
Coauthor Index
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last updated on 2024-08-05 20:21 CEST by the dblp team
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