The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 514).
Nikolaos Mylonas, Stamatis Karlos, and Grigorios Tsoumakas. 2020. Zero-Shot Classification of Biomedical Articles with Emerging MeSH Descriptors. In 11th Hellenic Conference on Artificial Intelligence (SETN 2020). Association for Computing Machinery, New York, NY, USA, 175–184. https://doi.org/10.1145/3411408.3411414
Nikolaos Mylonas, Stamatis Karlos, and Grigorios Tsoumakas. 2021. A multi-instance multi-label weakly supervised approach for dealing with emerging MeSH descriptors. In 19th International Conference on Artificial Intelligence in Medicine (AIME 2021). Best paper award. https://doi.org/10.1007/978-3-030-77211-6_47
Stamatis Karlos, Nikolaos Mylonas, and Grigorios Tsoumakas. 2021. Instance-Based Zero-Shot Learning for Semi-Automatic MeSH Indexing. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2021.08.009
Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos, and Grigorios Tsoumakas. 2022. ETHOS: a multi-label hate speech detection dataset. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-021-00608-2
Avraam Bardos, Ioannis Mollas, Nick Bassiliades, and Grigorios Tsoumakas. 2022. Local Explanation of Dimensionality Reduction. In Proceedings of the 12th Hellenic Conference on Artificial Intelligence (SETN 2022). Association for Computing Machinery, New York, NY, USA, Article 29, 1–9. https://doi.org/10.1145/3549737.3549770
Nikolaos Mylonas, Ioannis Mollas, Nick Bassiliades, and Grigorios Tsoumakas. 2022. Local Multi-Label Explanations for Random Forest. In Proceedings of the 4th eXplainable Knowledge Discovery in Data Mining (XKDD) workshop of the ECML PKDD 2022 conference. https://doi.org/10.1007/978-3-031-23618-1_25
Nikolaos Mylonas, Stamatis Karlos, and Grigorios Tsoumakas. 2023. WeakMeSH: Leveraging Provenance Information for Weakly Supervised Classification of Biomedical Articles with Emerging MeSH Descriptors. Artificial Intelligence on Medicine (AIM), Elsevier. https://doi.org/10.1016/j.artmed.2023.102505
Nikolaos Mylonas, Ioannis Mollas, Bin Liu, Yannis Manolopoulos, and Grigorios Tsoumakas. 2023. On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues. IEEE Intelligent Systems. https://doi.org/10.1109/MIS.2023.3255591
Nikolaos Mylonas, Ioannis Mollas, and Grigorios Tsoumakas. 2023. Beyond Annual Revisions: A Multi-Label Concept Drift Analysis of MeSH. In Proceedings of the 36th International Symposium on Computer Based Medical Systems (CBMS 2023). IEEE. Check the Publication Here
Nikolaos Mylonas, Ioannis Mollas, and Grigorios Tsoumakas. 2023. An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classification. ECMLPKDD 2023, Journal Track, Data Mining & Knowledge Discovery. Springer. Check the Publication Here
Sofia Katsaki, Christos Aivazidis, Nikolaos Mylonas, Ioannis Mollas, and Grigorios Tsoumakas. 2023. On the Adaptability of Attention-Based Interpretability in Different Transformer Architectures for Multi-Class Classification Tasks. ECMLPKDD 2023, 4th AIMLAI Workshop. Springer. Publication not available yet
Avraam Bardos, Nikolaos Mylonas, Ioannis Mollas, and Grigorios Tsoumakas. 2023. Local interpretability of random forests for multi-target regression. ECMLPKDD 2023, 4th AIMLAI Workshop. Springer. Check the Publication Here
Dimitrios Akrivousis, Nikolaos Mylonas, Ioannis Mollas, and Grigorios Tsoumakas. 2023. Text classification is keyphrase explainable! Exploring local interpretability of transformer models with keyphrase extraction. DSAA 2023, PRAXAI.Publication not available yet