no code implementations • 24 Jan 2023 • Ana Kostovska, Diederick Vermetten, Sašo Džeroski, Panče Panov, Tome Eftimov, Carola Doerr
In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.
no code implementations • 23 Nov 2022 • Ana Kostovska, Jasmin Bogatinovski, Andrej Treven, Sašo Džeroski, Dragi Kocev, Panče Panov
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 methods that appear in the literature.
no code implementations • 21 Nov 2022 • Ana Kostovska, Diederick Vermetten, Carola Doerr, Saso Džeroski, Panče Panov, Tome Eftimov
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research.
no code implementations • 21 Nov 2022 • Ana Kostovska, Carola Doerr, Sašo Džeroski, Dragi Kocev, Panče Panov, Tome Eftimov
To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task.
1 code implementation • 21 Jan 2022 • Ivica Dimitrovski, Ivan Kitanovski, Panče Panov, Nikola Simidjievski, Dragi Kocev
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets.
no code implementations • 4 Aug 2021 • Matej Petković, Luke Lucas, Tomaž Stepišnik, Panče Panov, Nikola Simidjievski, Dragi Kocev
The Mars Express (MEX) spacecraft has been orbiting Mars since 2004.
no code implementations • 3 Aug 2021 • Ana Kostovska, Matej Petković, Tomaž Stepišnik, Luke Lucas, Timothy Finn, José Martínez-Heras, Panče Panov, Sašo Džeroski, Alessandro Donati, Nikola Simidjievski, Dragi Kocev
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data.
no code implementations • 24 Apr 2021 • Ana Kostovska, Diederick Vermetten, Carola Doerr, Sašo Džeroski, Panče Panov, Tome Eftimov
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research.
no code implementations • 14 Jul 2018 • Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments.