67 citations · 283 across the 39 of their papers we have counts for
6 papers · 1 filter
FAIRification of MLC data
Ana Kostovska, Jasmin Bogatinovski, Andrej Treven +3
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and method…
OPTION: OPTImization Algorithm Benchmarking ONtology
Ana Kostovska, Diederick Vermetten, Carola Doerr +3
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use diff…
Explainable Model-specific Algorithm Selection for Multi-Label Classification
Ana Kostovska, Carola Doerr, Sašo Džeroski +3
Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest i…
Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification
Jurica Levatić, Michelangelo Ceci, Dragi Kocev +1
Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled examples, but also unlabeled examples. While SSL for the simple tasks of cl…
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
Ana Kostovska, Diederick Vermetten, Sašo Džeroski +3
Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm…
Boosting the Performance of Quantum Annealers using Machine Learning
Jure Brence, Dragan Mihailović, Viktor Kabanov +3
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commerc…