Celine Vens

发表

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, 2019, Comput. Educ..

Deep tree-ensembles for multi-output prediction pdf

Konstantinos Pliakos, Celine Vens, Felipe Kenji Nakano, 2020, ArXiv.

The feasibility of co-existence between conventional and genetically modified crops: Using machine learning to analyse the output of simulation models

Celine Vens, Aneta Ivanovska, Nathalie Colbach, 2008 .

Combining instance and feature neighbours for extreme multi-label classification

Len Feremans, Boris Cule, Celine Vens, 2020, International Journal of Data Science and Analytics.

First order random forests with complex aggregates

Celine Vens, Hendrik Blockeel, Anneleen Van Assche, 2004 .

Complex Aggregates over Clusters of Elements

Yvan Saeys, Sofie Van Gassen, Tom Dhaene, 2014, ILP.

Meta-learning from experiment databases : an illustration

Celine Vens, Hendrik Blockeel, Joaquin Vanschoren, 2007 .

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

Konstantinos Pliakos, Celine Vens, C. Vens, 2020, BMC Bioinformatics.

On heuristics for building model trees

Celine Vens, Hendrik Blockeel, 2005 .

Top-down phylogenetic tree reconstruction

Celine Vens, Hendrik Blockeel, Eduardo De Paula Costa, 2009 .

ReMauve: A Relational Model Tree Learner

Celine Vens, Jan Ramon, Hendrik Blockeel, 2006, ILP.

A simple regression based heuristic for learning model trees

Celine Vens, Hendrik Blockeel, 2006, Intell. Data Anal..

Combining Instance and Feature Neighbors for Efficient Multi-label Classification

Len Feremans, Boris Cule, Bart Goethals, 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Active learning for hierarchical multi-label classification

Celine Vens, Felipe Kenji Nakano, Ricardo Cerri, 2020, Data Mining and Knowledge Discovery.

FloReMi: Flow density survival regression using minimal feature redundancy

Yvan Saeys, Tom Dhaene, Celine Vens, 2016, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

Learning HMMs for nucleotide sequences from amino acid alignments

Celine Vens, Ricardo Cerri, Carlos Fischer, 2015, Bioinform..

Using clustering trees for learning phylogenetic trees

Celine Vens, Hendrik Blockeel, H. Blockeel, 2007 .

Predicting gene function using hierarchical multi-label decision tree ensembles

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2010, BMC Bioinformatics.

Partitioning the target space in multi-output learning

Celine Vens, Ricardo Cerri, Márcio P. Basgalupp, 2016 .

Complex aggregates in relational learning ; Complexe aggregaten in relationeel leren

Celine Vens, 2007 .

Generalizing from Example Clusters

Celine Vens, Hendrik Blockeel, Pan Hu, 2013, Discovery Science.

Mining features for biomedical data using clustering tree ensembles

Konstantinos Pliakos, Celine Vens, 2018, J. Biomed. Informatics.

The ACE Data Mining System User's Manual

Celine Vens, Jan Ramon, Hendrik Blockeel, 2009 .

Refining Aggregate Conditions in Relational Learning

Celine Vens, Jan Ramon, Hendrik Blockeel, 2006, PKDD.

Studying the Presence of Genetically Modified Variants in Organic Oilseed Rape by Using Relational Data Mining

Saso Dzeroski, Celine Vens, Aneta Ivanovska, 2007, EnviroInfo.

On heuristics for learning model trees

Celine Vens, Hendrik Blockeel, 2003 .

Using ILP to study the presence of genetically modified variants in organic oil seed rape

Saso Dzeroski, Celine Vens, Aneta Ivanovska, 2006 .

Network inference with ensembles of bi-clustering trees

Konstantinos Pliakos, Celine Vens, C. Vens, 2019, BMC Bioinformatics.

Top-Down Clustering for Protein Subfamily Identification

Celine Vens, Hendrik Blockeel, Eduardo P. Costa, 2013, Evolutionary bioinformatics online.

Outlier detection in relational data: A case study in geographical information systems

Celine Vens, Patrick De Causmaecker, Hendrik Blockeel, 2012, Expert Syst. Appl..

Predicting gene function in S. cerevisiae and A. thaliana using hierarchical multi-label decision tree ensembles

Celine Vens, Hendrik Blockeel, Jan Struyf, 2008 .

Ensembles of Multi-Objective Decision Trees

Saso Dzeroski, Celine Vens, Jan Struyf, 2007, ECML.

Top-Down Induction of Phylogenetic Trees

Celine Vens, Hendrik Blockeel, Eduardo P. Costa, 2010, EvoBIO.

Annotating transposable elements in the genome using relational decision tree ensembles

Celine Vens, Jan Ramon, Hendrik Blockeel, 2013, ILP 2013.

Top-down phylogenetic tree reconstruction: a decision tree approach

Celine Vens, Hendrik Blockeel, Eduardo De Paula Costa, 2009 .

Predicting Gene Function using Predictive Clustering Trees

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2010, Inductive Databases and Constraint-Based Data Mining.

Tree based feature induction for biomedical data

Konstantinos Pliakos, Celine Vens, 2016 .

Random forests for combining selection and aggregation in relational data mining

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2004 .

A Random Forest Approach to Relational Learning

Celine Vens, Hendrik Blockeel, Anneleen Van Assche, 2005, ICML 2005.

Complex aggregates in relational learning

Celine Vens, 2008, AI Commun..

Machine learning for discovering missing or wrong protein function annotations

Celine Vens, Felipe Kenji Nakano, Mathias Lietaert, 2019, BMC Bioinformatics.

Napovedovanje funkcij genov z induktivnim logicnim programiranjem in drevesi za napovedno razvrscanje

Celine Vens, Hendrik Blockeel, Sašo Džeroski, 2005 .

Identifying discriminative classification-based motifs in biological sequences

Celine Vens, Etienne G. J. Danchin, Marie-Noëlle Rosso, 2011, Bioinform..

Feature Induction based on Extremely Randomized Tree Paths

Konstantinos Pliakos, Celine Vens, 2016 .

Predicting human olfactory perception from chemical features of odor molecules

Amit Dhurandhar, Celine Vens, Yuanfang Guan, 2017, Science.

Improving interaction prediction exploiting background information

Konstantinos Pliakos, Celine Vens, 2016 .

Network representation with clustering tree features

Konstantinos Pliakos, Celine Vens, 2018, Journal of Intelligent Information Systems.

Labelling strategies for hierarchical multi-label classification techniques

Celine Vens, Isaac Triguero, C. Vens, 2016, Pattern Recognit..

Semi-supervised Clustering with Example Clusters

Celine Vens, Hendrik Blockeel, Bart Verstrynge, 2013, KDIR/KMIS.

Decision trees for hierarchical classification of transposable elements

Rafael Gomes Mantovani, Celine Vens, Ricardo Cerri, 2016 .

Learning aggregations and selections with relational neural networks

Celine Vens, Hendrik Blockeel, Anneleen Van Assche, 2005 .

Global multi-output decision trees for interaction prediction

Pierre Geurts, Konstantinos Pliakos, Celine Vens, 2018, Machine Learning.

Tree ensembles for predicting structured outputs

Saso Dzeroski, Celine Vens, Jan Struyf, 2013, Pattern Recognit..

Applying predictive clustering trees to the inductive logic programming 2005 challenge data

Celine Vens, Hendrik Blockeel, Sašo Džeroski, 2005 .

Fair Multi-Stakeholder News Recommender System with Hypergraph ranking pdf

Konstantinos Pliakos, Celine Vens, Alireza Gharahighehi, 2020, Inf. Process. Manag..

REPRESENTATIONAL POWER OF GENE FEATURES FOR FUNCTION PREDICTION

Konstantinos Pliakos, Celine Vens, Dragi Kocev, 2015 .

Aggregation and selection in relational data mining

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2004 .

Random Forest Based Feature Induction

Celine Vens, Fabrizio Costa, Fabrizio Costa, 2011, 2011 IEEE 11th International Conference on Data Mining.

Making Session-based News Recommenders Diversity-aware

Celine Vens, Alireza Gharahighehi, 2020, OHARS@RecSys.

First Order Random Forests with Complex Aggregates

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2004, ILP.

News Topic Recommendation Using an Extended Bayesian Personalized Ranking

Celine Vens, Alireza Gharahighehi, 2019, BNAIC/BENELEARN.

Decision trees for hierarchical multi-label classification

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2008, Machine Learning.

Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning

Grigorios Tsoumakas, Konstantinos Pliakos, Celine Vens, 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery pdf

Grigorios Tsoumakas, Konstantinos Pliakos, Celine Vens, 2020, ArXiv.

A machine learning based framework to identify and classify long terminal repeat retrotransposons

Celine Vens, Jan Ramon, Hendrik Blockeel, 2018, PLoS Comput. Biol..

First order random forests: Learning relational classifiers with complex aggregates

Saso Dzeroski, Celine Vens, Hendrik Blockeel, 2006, Machine Learning.

Feature Induction and Network Mining with Clustering Tree Ensembles

Konstantinos Pliakos, Celine Vens, 2016, NFMCP@PKDD/ECML.

Diversification in session-based news recommender systems pdf

Celine Vens, Alireza Gharahighehi, C. Vens, 2021, Personal and Ubiquitous Computing.

Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery pdf

Grigorios Tsoumakas, Konstantinos Pliakos, Celine Vens, 2021, Applied Intelligence.

Fair Multi-Stakeholder News Recommender System with Hypergraph ranking

Konstantinos Pliakos, Celine Vens, Alireza Gharahighehi, 2021, Inf. Process. Manag..

Beyond global and local multi-target learning

Celine Vens, Ricardo Cerri, Márcio P. Basgalupp, 2021, Inf. Sci..

An Ensemble Hypergraph Learning Framework for Recommendation

Konstantinos Pliakos, Celine Vens, Alireza Gharahighehi, 2021, DS.

Transposable element annotation using relational random forests

Celine Vens, Jan Ramon, Hendrik Blockeel, 2013 .

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Jung-Hsien Chiang, Devin C. Koestler, Celine Vens, 2018, Scientific Reports.