Gilles Vandewiele

发表

Towards an automated workout compliance model

Steven Verstockt, Filip De Turck, Femke Ongenae, 2018 .

Data Mining in the Development of Mobile Health Apps: Assessing In-App Navigation Through Markov Chain Analysis (Preprint)

F. Ongenae, F. De Turck, Jeroen Stragier, 2018 .

R-GCN: The R Could Stand for Random

F. Ongenae, S. Hoecke, Gilles Vandewiele, 2022, ArXiv.

Do Not Sleep on Linear Models (cid:63) Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring

Jonas Van Der Donckt, S. Hoecke, Gilles Vandewiele, 2022 .

The web-based application “QUiPP v.2” for the prediction of preterm birth in symptomatic women is not yet ready for worldwide clinical use: ten reflections on development, validation and use

J. Decruyenaere, Johan Steen, K. Roelens, 2022, Archives of Gynecology and Obstetrics.

Training Progression in Recreational Cyclists: No Linear Dose-Response Relationship With Training Load

J. Bourgois, J. Boone, Gilles Vandewiele, 2019, Journal of strength and conditioning research.

How do Users interact with Mobile Health Apps?: A Markov Chain Analysis

F. Ongenae, F. D. Backere, J. Heyse, 2020, PervasiveHealth.

Deep learning models for predicting RNA degradation via dual crowdsourcing

Do Soon Kim, John J. Nicol, Rhiju Das, 2021, ArXiv.

Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs pdf

Filip De Turck, Heiko Paulheim, Pieter Bonte, 2020, DEXA Workshops.

Facilitating the Analysis of COVID-19 Literature Through a Knowledge Graph

Filip De Turck, Femke Ongenae, Anastasia Dimou, 2020, SEMWEB.

Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer: Unraveling the Puzzle of Predictive Indicators.

F. Ongenae, S. Matthys, J. Bourgois, 2019, International journal of sports physiology and performance.

MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph

Filip De Turck, Femke Ongenae, Bram Steenwinckel, 2020, BMC Medical Informatics and Decision Making.

Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph

Filip De Turck, Femke Ongenae, Bram Steenwinckel, 2019, SEPDA@ISWC.

A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles

Filip De Turck, Sofie Van Hoecke, Femke Ongenae, 2017, PAKDD.

GENESIM: genetic extraction of a single, interpretable model pdf

Filip De Turck, Sofie Van Hoecke, Femke Ongenae, 2016, NIPS 2016.

Tslearn, A Machine Learning Toolkit for Time Series Data

Marc Rußwurm, Romain Tavenard, Marie Payne, 2020, J. Mach. Learn. Res..

INK: knowledge graph embeddings for node classification

F. Turck, F. Ongenae, Gilles Vandewiele, 2022, Data Mining and Knowledge Discovery.

INK: knowledge graph embeddings for node classification

F. Turck, F. Ongenae, Gilles Vandewiele, 2022, Data Min. Knowl. Discov..

Clinical information extraction for preterm birth risk prediction

Thomas Demeester, Femke Ongenae, Filip De Turck, 2020, J. Biomed. Informatics.

Data Mining in the Development of Mobile Health Apps: Assessing In-App Navigation Through Markov Chain Analysis

Femke Ongenae, Filip De Turck, Lieven De Marez, 2018, Journal of medical Internet research.

Time-to-Birth Prediction Models and the Influence of Expert Opinions

Filip De Turck, Thomas Demeester, Sofie Van Hoecke, 2019, AIME.

Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling

F. Turck, F. Ongenae, F. D. Backere, 2020, Artif. Intell. Medicine.

CVS2KG: Transforming Tabular Data into Semantic Knowledge

Filip De Turck, Femke Ongenae, Bram Steenwinckel, 2019, SemTab@ISWC.

Real-Time data dissemination and analytics platform for challenging IoT environments

Jeroen Famaey, Steven Latré, Esteban Municio, 2017, 2017 Global Information Infrastructure and Networking Symposium (GIIS).

A decision support system to follow up and diagnose primary headache patients using semantically enriched data

Femke Ongenae, Filip De Turck, Vincent Keereman, 2018, BMC Medical Informatics and Decision Making.

Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring

N. Vandenbussche, Gilles Vandewiele, Emiel Deprost, 2022, Biomed. Signal Process. Control..

REFORMS: Reporting Standards for Machine Learning Based Science

Inioluwa Deborah Raji, Matthew J. Salganik, Odd Erik Gundersen, 2023, ArXiv.

pyRDF2Vec: A Python Implementation and Extension of RDF2Vec

F. Ongenae, Gilles Vandewiele, Bram Steenwinckel, 2022, ESWC.

Predicting Train Occupancies based on Query Logs and External Data Sources

Ruben Verborgh, Erik Mannens, Filip De Turck, 2017, WWW.

Predictive models of RNA degradation through dual crowdsourcing

Michele Tinti, Youhan Lee, Andrew M. Watkins, 2021, ArXiv.

Checklist for reporting ML-based science

Matthew J. Salganik, M. Lones, Brandon M Stewart, 2023 .

A Critical Look at Studies Applying Over-Sampling on the TPEHGDB Dataset

Filip De Turck, Thomas Demeester, Sofie Van Hoecke, 2019, AIME.

REFORMS: Reporting Standards for Machine Learning Based Science

Inioluwa Deborah Raji, Matthew J. Salganik, Odd Erik Gundersen, 2023, ArXiv.

Enhancing White-Box Machine Learning Processes by Incorporating Semantic Background Knowledge

Gilles Vandewiele, Gilles Vandewiele, 2017, ESWC.

GENDIS: Genetic Discovery of Shapelets pdf

Filip De Turck, Femke Ongenae, Gilles Vandewiele, 2019, Sensors.

Data Mining in the Development of Mobile Health Apps: Assessing In-App Navigation Through Markov Chain Analysis

F. Ongenae, Jeroen Stragier, Wendy Van den Broeck, 2018, Journal of medical Internet research.