Celine Vens

According to our database, Celine Vens authored at least 64 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., October, 2023

Diversification in session-based news recommender systems.

Alireza Gharahighehi, Celine Vens

Pers. Ubiquitous Comput., 2023

HypeRS: Building a Hypergraph-driven ensemble Recommender System.

Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos

CoRR, 2023

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification.

Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens

Comput. Biol. Medicine, 2023

Exploiting Censored Information in Self-Training for Time-to-Event Prediction.

Fateme Nateghi Haredasht, Kazeem Adesina Dauda, Celine Vens

IEEE Access, 2023

BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor.

Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

IEEE Access, 2023

Extending Bayesian Personalized Ranking with Survival Analysis for MOOC Recommendation.

Alireza Gharahighehi, Michela Venturini, Achilleas Ghinis, Frederik Cornillie, Celine Vens

Proceedings of the Adjunct Proceedings of the 31st ACM Conference on User Modeling, 2023

Active Learning for Survival Analysis with Incrementally Disclosed Label Information.

Klest Dedja, Felipe Kenji Nakano, Celine Vens

Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023), 2023

A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis.

Robbe D'hondt, Sinead Moylett, An Goris, Celine Vens

Proceedings of the Artificial Intelligence in Medicine, 2023

2022

Deep tree-ensembles for multi-output prediction.

Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

Pattern Recognit., 2022

Predicting Survival Outcomes in the Presence of Unlabeled Data.

Fateme Nateghi Haredasht, Celine Vens

Mach. Learn., 2022

Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification.

Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens

CoRR, 2022

Explaining random forest prediction through diverse rulesets.

Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

CoRR, 2022

Survival analysis with semi-supervised predictive clustering trees.

Bijit Roy, Tomaz Stepisnik, The Pooled Resource Open-Access A. L. S. Clinical Trials Consortium, Celine Vens, Saso Dzeroski

Comput. Biol. Medicine, 2022

Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery.

Bin Liu, Konstantinos Pliakos, Celine Vens, Grigorios Tsoumakas

Appl. Intell., 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

An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering.

Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens

Proceedings of the Intelligent Systems and Applications, 2022

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

Proceedings of the Artificial Intelligence in Medicine, 2022

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

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems.

Alireza Gharahighehi, Celine Vens

SN Comput. Sci., 2021

Beyond global and local multi-target learning.

Márcio P. Basgalupp, Ricardo Cerri, Leander Schietgat, Isaac Triguero, Celine Vens

Inf. Sci., 2021

Fair multi-stakeholder news recommender system with hypergraph ranking.

Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos

Inf. Process. Manag., 2021

Explaining a Random Survival Forest by Extracting Prototype Rules.

Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

An Ensemble Hypergraph Learning Framework for Recommendation.

Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos

Proceedings of the Discovery Science - 24th International Conference, 2021

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

Active learning for hierarchical multi-label classification.

Felipe Kenji Nakano, Ricardo Cerri, Celine Vens

Data Min. Knowl. Discov., 2020

Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.

Konstantinos Pliakos, Celine Vens

BMC Bioinform., 2020

Making Session-based News Recommenders Diversity-aware.

Alireza Gharahighehi, Celine Vens

Proceedings of the Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020), 2020

Predictive Bi-clustering Trees for Hierarchical Multi-label Classification.

Bruna Zamith Santos, Felipe Kenji Nakano, Ricardo Cerri, Celine Vens

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Multi-stakeholder News Recommendation Using Hypergraph Learning.

Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos

Proceedings of the ECML PKDD 2020 Workshops, 2020

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

Network inference with ensembles of bi-clustering trees.

Konstantinos Pliakos, Celine Vens

BMC Bioinform., 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

Extended Bayesian Personalized Ranking Based on Consumption Behavior.

Alireza Gharahighehi, Celine Vens

Proceedings of the Artificial Intelligence and Machine Learning, 2019

News Topic Recommendation Using an Extended Bayesian Personalized Ranking.

Alireza Gharahighehi, Celine Vens

Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

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

Global multi-output decision trees for interaction prediction.

Konstantinos Pliakos, Pierre Geurts, Celine Vens

Mach. Learn., 2018

Network representation with clustering tree features.

Konstantinos Pliakos, Celine Vens

J. Intell. Inf. Syst., 2018

Mining features for biomedical data using clustering tree ensembles.

Konstantinos Pliakos, Celine Vens

J. Biomed. Informatics, 2018

Teaching for Versus Through Problem Solving: Impact on Teaching and Learning.

Annelies Raes, Pieter Vanneste, Celine Vens, Fien Depaepe

Proceedings of the Rethinking learning in the digital age: Making the Learning Sciences count, 2018

2017

Combining Instance and Feature Neighbors for Efficient Multi-label Classification.

Len Feremans, Boris Cule, Celine Vens, Bart Goethals

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

2016

Labelling strategies for hierarchical multi-label classification techniques.

Isaac Triguero, Celine Vens

Pattern Recognit., 2016

Feature Induction and Network Mining with Clustering Tree Ensembles.

Konstantinos Pliakos, Celine Vens

Proceedings of the New Frontiers in Mining Complex Patterns - 5th International Workshop, 2016

2015

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

2014

Complex Aggregates over Clusters of Elements.

Celine Vens, Sofie Van Gassen, Tom Dhaene, Yvan Saeys

Proceedings of the Inductive Logic Programming - 24th International Conference, 2014

2013

Tree ensembles for predicting structured outputs.

Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski

Pattern Recognit., 2013

Semi-supervised Clustering with Example Clusters.

Celine Vens, Bart Verstrynge, Hendrik Blockeel

Proceedings of the KDIR/KMIS 2013 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing, Vilamoura, Algarve, Portugal, 19, 2013

Generalizing from Example Clusters.

Pan Hu, Celine Vens, Bart Verstrynge, Hendrik Blockeel

Proceedings of the Discovery Science - 16th International Conference, 2013

2012

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

2011

Identifying discriminative classification-based motifs in biological sequences.

Celine Vens, Marie-Noëlle Rosso, Etienne G. J. Danchin

Bioinform., 2011

Random Forest Based Feature Induction.

Celine Vens, Fabrizio Costa

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

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

Top-Down Induction of Phylogenetic Trees.

Celine Vens, Eduardo P. Costa, Hendrik Blockeel

Proceedings of the Evolutionary Computation, 2010

Predicting Gene Function using Predictive Clustering Trees.

Celine Vens, Leander Schietgat, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Saso Dzeroski

Proceedings of the Inductive Databases and Constraint-Based Data Mining., 2010

2008

Decision trees for hierarchical multi-label classification.

Celine Vens, Jan Struyf, Leander Schietgat, Saso Dzeroski, Hendrik Blockeel

Mach. Learn., 2008

Complex aggregates in relational learning.

Celine Vens

AI Commun., 2008

2007

Complex aggregates in relational learning ; Complexe aggregaten in relationeel leren.

Celine Vens

PhD thesis, 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

Proceedings of the Environmental Informatics and Systems Research: Proceedings of the 21st International Conference for Environmental Protection, 2007

Ensembles of Multi-Objective Decision Trees.

Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski

Proceedings of the Machine Learning: ECML 2007, 2007

2006

First order random forests: Learning relational classifiers with complex aggregates.

Anneleen Van Assche, Celine Vens, Hendrik Blockeel, Saso Dzeroski

Mach. Learn., 2006

A simple regression based heuristic for learning model trees.

Celine Vens, Hendrik Blockeel

Intell. Data Anal., 2006

Refining Aggregate Conditions in Relational Learning.

Celine Vens, Jan Ramon, Hendrik Blockeel

Proceedings of the Knowledge Discovery in Databases: PKDD 2006, 2006

ReMauve: A Relational Model Tree Learner.

Celine Vens, Jan Ramon, Hendrik Blockeel

Proceedings of the Inductive Logic Programming, 16th International Conference, 2006

2004

First Order Random Forests with Complex Aggregates.

Celine Vens, Anneleen Van Assche, Hendrik Blockeel, Saso Dzeroski

Proceedings of the Inductive Logic Programming, 14th International Conference, 2004