First Order Random Forests with Complex Aggregates. - Celine Vens; Anneleen Van Assche; Hendrik Blockeel; Saso Dzeroski (ILP, 2004)

ReMauve: A Relational Model Tree Learner. - Celine Vens; Jan Ramon; Hendrik Blockeel (ILP, 2006)

Refining Aggregate Conditions in Relational Learning. - Celine Vens; Jan Ramon; Hendrik Blockeel (PKDD, 2006)

A simple regression based heuristic for learning model trees. - Celine Vens; Hendrik Blockeel (Intell. Data Anal., 2006)

First order random forests: Learning relational classifiers with complex aggregates. - Anneleen Van Assche; Celine Vens; Hendrik Blockeel; Saso Dzeroski (Mach. Learn., 2006)

Ensembles of Multi-Objective Decision Trees. - Dragi Kocev; Celine Vens; Jan Struyf; Saso Dzeroski (ECML, 2007)

Studying the Presence of Genetically Modified Variants in Organic Oilseed Rape by Using Relational Data Mining. - Aneta Ivanovska; Celine Vens; Saso Dzeroski; Nathalie Colbach (EnviroInfo (1), 2007)

Complex aggregates in relational learning ; Complexe aggregaten in relationeel leren. - Celine Vens (2007)

Complex aggregates in relational learning. - Celine Vens (AI Commun., 2008)

Decision trees for hierarchical multi-label classification. - Celine Vens; Jan Struyf; Leander Schietgat; Saso Dzeroski; Hendrik Blockeel (Mach. Learn., 2008)

Predicting Gene Function using Predictive Clustering Trees. - Celine Vens; Leander Schietgat; Jan Struyf; Hendrik Blockeel; Dragi Kocev; Saso Dzeroski (Inductive Databases and Constraint-Based Data Mining, 2010)

Top-Down Induction of Phylogenetic Trees. - Celine Vens; Eduardo P. Costa; Hendrik Blockeel (EvoBIO, 2010)

Predicting gene function using hierarchical multi-label decision tree ensembles. - Leander Schietgat; Celine Vens; Jan Struyf; Hendrik Blockeel; Dragi Kocev; Saso Dzeroski (BMC Bioinform., 2010)

Random Forest Based Feature Induction. - Celine Vens; Fabrizio Costa (ICDM, 2011)

Identifying discriminative classification-based motifs in biological sequences. - Celine Vens; Marie-Noëlle Rosso; Etienne G. J. Danchin (Bioinform., 2011)

Outlier detection in relational data: A case study in geographical information systems. - Joris Maervoet; Celine Vens; Greet Vanden Berghe; Hendrik Blockeel; Patrick De Causmaecker (Expert Syst. Appl., 2012)

Generalizing from Example Clusters. - Pan Hu; Celine Vens; Bart Verstrynge; Hendrik Blockeel (Discovery Science, 2013)

Semi-supervised Clustering with Example Clusters. - Celine Vens; Bart Verstrynge; Hendrik Blockeel (KDIR/KMIS, 2013)

Tree ensembles for predicting structured outputs. - Dragi Kocev; Celine Vens; Jan Struyf; Saso Dzeroski (Pattern Recognit., 2013)

Complex Aggregates over Clusters of Elements. - Celine Vens; Sofie Van Gassen; Tom Dhaene; Yvan Saeys (ILP, 2014)

Learning HMMs for nucleotide sequences from amino acid alignments. - Carlos N. Fischer; Claudia M. A. Carareto; Renato Augusto Corrêa dos Santos; Ricardo Cerri; Eduardo P. Costa; Leander Schietgat; Celine Vens (Bioinform., 2015)

Feature Induction and Network Mining with Clustering Tree Ensembles. - Konstantinos Pliakos; Celine Vens (NFMCP@PKDD/ECML, 2016)

Labelling strategies for hierarchical multi-label classification techniques. - Isaac Triguero; Celine Vens (Pattern Recognit., 2016)

Combining Instance and Feature Neighbors for Efficient Multi-label Classification. - Len Feremans; Boris Cule; Celine Vens; Bart Goethals (DSAA, 2017)

Teaching for Versus Through Problem Solving: Impact on Teaching and Learning. - Annelies Raes; Pieter Vanneste; Celine Vens; Fien Depaepe (ICLS, 2018)

Mining features for biomedical data using clustering tree ensembles. - Konstantinos Pliakos; Celine Vens (J. Biomed. Informatics, 2018)

Network representation with clustering tree features. - Konstantinos Pliakos; Celine Vens (J. Intell. Inf. Syst., 2018)

Global multi-output decision trees for interaction prediction. - Konstantinos Pliakos; Pierre Geurts; Celine Vens (Mach. Learn., 2018)

A machine learning based framework to identify and classify long terminal repeat retrotransposons. - Leander Schietgat; Celine Vens; Ricardo Cerri; Carlos N. Fischer; Eduardo P. Costa; Jan Ramon; Claudia M. A. Carareto; Hendrik Blockeel (PLoS Comput. Biol., 2018)

News Topic Recommendation Using an Extended Bayesian Personalized Ranking. - Alireza Gharahighehi; Celine Vens (BNAIC/BENELEARN, 2019)

Extended Bayesian Personalized Ranking Based on Consumption Behavior. - Alireza Gharahighehi; Celine Vens (BNAIC/BENELEARN (Selected Papers), 2019)

Machine learning for discovering missing or wrong protein function annotations - A comparison using updated benchmark datasets. - Felipe Kenji Nakano; Mathias Lietaert; Celine Vens (BMC Bioinform., 2019)

Network inference with ensembles of bi-clustering trees. - Konstantinos Pliakos; Celine Vens (BMC Bioinform., 2019)

Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. - Konstantinos Pliakos; Seang-Hwane Joo; Jung Yeon Park; Frederik Cornillie; Celine Vens; Wim van den Noortgate (Comput. Educ., 2019)

Multi-stakeholder News Recommendation Using Hypergraph Learning. - Alireza Gharahighehi; Celine Vens; Konstantinos Pliakos (PKDD/ECML Workshops, 2020)

Predictive Bi-clustering Trees for Hierarchical Multi-label Classification. - Bruna Zamith Santos; Felipe Kenji Nakano; Ricardo Cerri; Celine Vens (ECML/PKDD (3), 2020)

Making Session-based News Recommenders Diversity-aware. - Alireza Gharahighehi; Celine Vens (OHARS@RecSys, 2020)

Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. - Konstantinos Pliakos; Celine Vens (BMC Bioinform., 2020)

Active learning for hierarchical multi-label classification. - Felipe Kenji Nakano; Ricardo Cerri; Celine Vens (Data Min. Knowl. Discov., 2020)

Combining instance and feature neighbours for extreme multi-label classification. - Len Feremans; Boris Cule; Celine Vens; Bart Goethals (Int. J. Data Sci. Anal., 2020)

An Ensemble Hypergraph Learning Framework for Recommendation. - Alireza Gharahighehi; Celine Vens; Konstantinos Pliakos (DS, 2021)

Explaining a Random Survival Forest by Extracting Prototype Rules. - Klest Dedja; Felipe Kenji Nakano; Konstantinos Pliakos; Celine Vens (PKDD/ECML Workshops (2), 2021)

Fair multi-stakeholder news recommender system with hypergraph ranking. - Alireza Gharahighehi; Celine Vens; Konstantinos Pliakos (Inf. Process. Manag., 2021)

Beyond global and local multi-target learning. - Márcio P. Basgalupp; Ricardo Cerri; Leander Schietgat; Isaac Triguero; Celine Vens (Inf. Sci., 2021)

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems. - Alireza Gharahighehi; Celine Vens (SN Comput. Sci., 2021)

Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. - Konstantinos Pliakos; Celine Vens; Grigorios Tsoumakas (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

A Novel Survival Analysis Approach to Predict the Need for Intubation in Intensive Care Units. - Michela Venturini; Ingrid Van Keilegom; Wouter De Corte; Celine Vens (AIME, 2022)

An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering. - Alireza Gharahighehi; Felipe Kenji Nakano; Celine Vens (IntelliSys (1), 2022)

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction. - Alireza Gharahighehi; Konstantinos Pliakos; Celine Vens (IEEE Access, 2022)

Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery. - Bin Liu 058; Konstantinos Pliakos; Celine Vens; Grigorios Tsoumakas (Appl. Intell., 2022)

Survival analysis with semi-supervised predictive clustering trees. - Bijit Roy; Tomaz Stepisnik; Pooled Resource Open-Access A. L. S. Clinical Trials Consortium; Celine Vens; Saso Dzeroski (Comput. Biol. Medicine, 2022)

Explaining random forest prediction through diverse rulesets. - Klest Dedja; Felipe Kenji Nakano; Konstantinos Pliakos; Celine Vens (CoRR, 2022)

Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification. - Miguel Romero 005; Felipe Kenji Nakano; Jorge Finke; Camilo Rocha; Celine Vens (CoRR, 2022)

Predicting Survival Outcomes in the Presence of Unlabeled Data. - Fateme Nateghi Haredasht; Celine Vens (Mach. Learn., 2022)

Deep tree-ensembles for multi-output prediction. - Felipe Kenji Nakano; Konstantinos Pliakos; Celine Vens (Pattern Recognit., 2022)

A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis. - Robbe D'hondt; Sinead Moylett; An Goris; Celine Vens (AIME, 2023)

Active Learning for Survival Analysis with Incrementally Disclosed Label Information. - Klest Dedja; Felipe Kenji Nakano; Celine Vens (IAL@PKDD/ECML, 2023)

Extending Bayesian Personalized Ranking with Survival Analysis for MOOC Recommendation. - Alireza Gharahighehi; Michela Venturini; Achilleas Ghinis; Frederik Cornillie; Celine Vens (UMAP (Adjunct Publication), 2023)

BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor. - Klest Dedja; Felipe Kenji Nakano; Konstantinos Pliakos; Celine Vens (IEEE Access, 2023)

Exploiting Censored Information in Self-Training for Time-to-Event Prediction. - Fateme Nateghi Haredasht; Kazeem Adesina Dauda; Celine Vens (IEEE Access, 2023)

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification. - Miguel Romero 005; Felipe Kenji Nakano; Jorge Finke; Camilo Rocha; Celine Vens (Comput. Biol. Medicine, 2023)

HypeRS: Building a Hypergraph-driven ensemble Recommender System. - Alireza Gharahighehi; Celine Vens; Konstantinos Pliakos (CoRR, 2023)

Diversification in session-based news recommender systems. - Alireza Gharahighehi; Celine Vens (Pers. Ubiquitous Comput., 2023)

Online Extra Trees Regressor. - Saulo Martiello Mastelini; Felipe Kenji Nakano; Celine Vens; André Carlos Ponce de Leon Ferreira de Carvalho (IEEE Trans. Neural Networks Learn. Syst., 2023)

Using Machine Learning to Predict the Number of Latent Skills in Online Learning Environments. - Changsheng Chen; Robbe D'hondt; Celine Vens; Wim van den Noortgate (ALL@AIED, 2024)

Course recommendations in MOOCs using collaborative filtering and survival analysis (Invited Paper). - Alireza Gharahighehi; Michela Venturini; Celine Vens (ALL@AIED, 2024)

Personalized Learning in K-12 Education: Exploring Weak-Labels for a Random Forest-based Collaborative Filtering Approach. - Pedro Ilidio; Alireza Gharahighehi; Felipe Kenji Nakano; Celine Vens (ALL@AIED, 2024)

Deep forests with tree-embeddings and label imputation for weak-label learning. - Pedro Ilidio; Ricardo Cerri; Celine Vens; Felipe Kenji Nakano (IJCNN, 2024)

Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches. - Michela Venturini; Fateme Nateghi Haredasht; Frantisek Sabovcik; Robert J. H. Miller; Tatiana Kuznetsova; Celine Vens (IEEE Access, 2024)

Predicting time-to-intubation after critical care admission using machine learning and cured fraction information. - Michela Venturini; Ingrid Van Keilegom; Wouter De Corte; Celine Vens (Artif. Intell. Medicine, 2024)

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission. - 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 (Comput. Methods Programs Biomed., 2024)

SurvivalLVQ: Interpretable supervised clustering and prediction in survival analysis via Learning Vector Quantization. - Jasper de Boer; Klest Dedja; Celine Vens (Pattern Recognit., 2024)

Multi-Event Survival-Informed Clustering for Time-to-Worsening in Multiple Sclerosis. - Robbe D'hondt; Jasper de Boer; Celine Vens (AIME (1), 2025)

Integrating Expert Knowledge in Matrix Factorization. - Changsheng Chen; Robbe D'hondt; Alireza Gharahighehi; Celine Vens; Wim van den Noortgate (LAK Workshops, 2025)

Explainable time-to-progression predictions in multiple sclerosis. - Robbe D'hondt; Klest Dedja; Sofie Aerts; Bart Van Wijmeersch; Tomas Kalincik; Stephen Reddel; Eva Kubala Havrdova; Alessandra Lugaresi; Bianca Weinstock-Guttman; Saloua Mrabet; Patrice Lalive; Allan G. Kermode; et al. (Comput. Methods Programs Biomed., 2025)

Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis. - Alireza Gharahighehi; Achilleas Ghinis; Michela Venturini; Frederik Cornillie; Celine Vens (CoRR, 2025)