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A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization
Gjorgjina Cenikj, Gašper Petelin, Tome Eftimov, 2024, original scientific article

Abstract: The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models’ generalizability in single-objective numerical optimization across diverse benchmark suites. Using Exploratory Landscape Analysis (ELA) and transformer-based (TransOpt) features, the research investigates their individual and combined effectiveness in AS across four benchmarks: BBOB, AFFINE, RANDOM, and ZIGZAG. AS models perform differently based on benchmark suite similarities in algorithm performance distributions and single-best solvers. When suites align, these models underperform against a baseline predicting mean algorithm performance; yet, they outperform the baseline when suites differ in performance distributions and solvers. The AS models trained using the ELA landscape features are better than the models trained using the TransOpt features on the BBOB and AFFINE benchmark suites, while the opposite is true for the RANDOM benchmark suite. Ultimately, the study reveals challenges in accurately capturing algorithm performance through problem landscape features (ELA or TransOpt), impacting AS model applicability.
Keywords: algorithm selection, multi-target regression, generalization, benchmarking
Published in DiRROS: 21.05.2024; Views: 240; Downloads: 348
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Assessing the generalizability of a performance predictive model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution

Keywords: algorithms, predictive models, machine learning
Published in DiRROS: 15.09.2023; Views: 460; Downloads: 298
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FooDis : a food-disease relation mining pipeline
Gjorgjina Cenikj, Tome Eftimov, Barbara Koroušić-Seljak, 2023, original scientific article

Abstract: Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx.
Keywords: text mining, relation extraction, named entity recognition, named entity linking, food-disease relations
Published in DiRROS: 25.05.2023; Views: 478; Downloads: 233
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From language models to large-scale food and biomedical knowledge graphs
Gjorgjina Cenikj, Lidija Strojnik, Risto Angelski, Nives Ogrinc, Barbara Koroušić-Seljak, Tome Eftimov, 2023, original scientific article

Abstract: Knowledge about the interactions between dietary and biomedical factors is scattered throughout uncountable research articles in an unstructured form (e.g., text, images, etc.) and requires automatic structuring so that it can be provided to medical professionals in a suitable format. Various biomedical knowledge graphs exist, however, they require further extension with relations between food and biomedical entities. In this study, we evaluate the performance of three state-of-the-art relation-mining pipelines (FooDis, FoodChem and ChemDis) which extract relations between food, chemical and disease entities from textual data. We perform two case studies, where relations were automatically extracted by the pipelines and validated by domain experts. The results show that the pipelines can extract relations with an average precision around 70%, making new discoveries available to domain experts with reduced human effort, since the domain experts should only evaluate the results, instead of finding, and reading all new scientific papers.
Keywords: biomedical knowledge graphs, relation-mining pipelines, relation extraction, validation
Published in DiRROS: 17.05.2023; Views: 552; Downloads: 212
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SciFoodNER : food named entity recognition for scientific text
Gjorgjina Cenikj, Gašper Petelin, Barbara Koroušić-Seljak, Tome Eftimov, 2022, published scientific conference contribution

Keywords: food, named entity recognition, named entity linking, information extraction
Published in DiRROS: 09.03.2023; Views: 586; Downloads: 358
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