VU Duomenų mokslo ir skaitmeninių technologijų instituto / Matematikos ir informatikos instituto autoriaus (-ių) 'Dalia Breskuvienė' publikacijų sąrašas (pagal Lietuvos akademinių bibliotekų tinklo (LABT) publikacijų bazes VUB [nuo 2011 m. iki dabar] ir LMAVB [iki 2010 m. imtinai]):
Publications of 'Dalia Breskuvienė', VU Institute of Data Science and Digital Technologies / Institute of Mathematics and Informatics (based on 1. Lithuanian Academic Library Network (LABT) database of Vilnius University Library [since 2011 until now] and 2. LABT database of Wroblewski Library of the Lithuanian Academy of Sciences [until 2010]):
Eil. Nr. Publikacija
1Breskuvienė, Dalia; Dzemyda, Gintautas. Highly imbalanced data case: pattern-guided feature selection to detect financial fraud // DAMSS: 15th conference on data analysis methods for software systems, Druskininkai, Lithuania, November 28-30, 2024. Vilnius : Vilniaus universiteto leidykla, 2024. eISBN 9786090711125. p. 12-13. (Vilnius University Proceedings, eISSN 2669-0233 ; vol. 52). DOI: 10.15388/DAMSS.15.2024.
2Maselienė, Tatjana; Žukienė, Guoda; Laurinavičienė, Anna; Breskuvienė, Dalia; Ramašauskaitė, Diana; Dženkevičiūtė, Vilma. Alterations in maternal cardiovascular parameters and their impact on uterine and fetal circulation in hypertensive pregnancies and fetal growth restriction // International journal of cardiology cardiovascular risk and prevention. Amsterdam : Elsevier. ISSN 2772-4875. 2024, vol. 22, art. no. 200316, p. [1-6]. DOI: 10.1016/j.ijcrp.2024.200316.
3Breskuvienė, Dalia; Dzemyda, Gintautas. What is a concept drift, and does it affect machine learning performance? // DAMSS: 14th conference on data analysis methods for software systems, Druskininkai, Lithuania, November 30 - December 2, 2023. Vilnius : Vilniaus universiteto leidykla, 2023. eISBN 9786090709856. p. 14. (Vilnius University Proceedings, eISSN 2669-0233 ; vol. 39). DOI: 10.15388/DAMSS.14.2023.
4Breskuvienė, Dalia; Dzemyda, Gintautas. Categorical feature encoding techniques for improved classifier performance when dealing with imbalanced data of fraudulent transactions // International journal of computers communications & control. Oradea : Agora University. ISSN 1841-9836. eISSN 1841-9844. 2023, vol. 18, iss. 3, art. no. 5433, p. [1-17]. DOI: 10.15837/ijccc.2023.3.5433.
5Breskuvienė, Dalia; Dzemyda, Gintautas. Imbalanced data classification approach based on clustered training set // Data science in applications /editors: Dzemyda, G., Bernatavičienė, J., Kacprzyk, J. Cham : Springer, 2023. ISBN 9783031244520. eISBN 9783031244537. p. 43-62. (Studies in Computational Intelligence, ISSN 1860-949X, eISSN 1860-9503 ; vol. 1084). DOI: 10.1007/978-3-031-24453-7_3.
6Breskuvienė, Dalia; Dzemyda, Gintautas. Autoencoder for fraudulent transactions data feature engineering // DAMSS: 13th conference on data analysis methods for software systems, Druskininkai, Lithuania, December 1–3, 2022. Vilnius : Vilniaus universiteto leidykla, 2022. ISBN 9786090707944. eISBN 9786090707951. p. 11. (Vilnius University Proceedings, eISSN 2669-0233 ; vol. 31). DOI: 10.15388/DAMSS.13.2022.
7Breskuvienė, Dalia; Dzemyda, Gintautas. Clustering-based optimization in fraud detection classifier training // EURO 2022: [32nd European Conference on Operational Research (EURO XXXII)], Espoo, Finland, July 3-6, 2022 : abstract book. Espoo : Aalto university, 2022. ISBN 9789519525419. p. 152. Prieiga per internetą: <https://www.euro-online.org/conf/admin/tmp/program-euro32.pdf>.