8 citations · 18 across the 16 of their papers we have counts for
7 papers · 1 filter
Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Maryam Gholami Shiri, Eva Tuba, Sašo Džeroski +2
Benchmarking deep learning (DL) models for multi-label classification (MLC) of remote sensing images (RSI) typically yields rankings that do not generalize beyond the evaluated dat…
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj +3
Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on i…
A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization
Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin +3
This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-ob…
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
Ana Nikolikj, Ana Kostovska, Gjorgjina Cenikj +2
This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem…
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten +6
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires b…
Assessing the Generalizability of a Performance Predictive Model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova +6
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive…
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
Ana Nikolikj, Michal Pluháček, Carola Doerr +2
Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously uns…