Improved imbalanced classification through convex space learning

Improved imbalanced classification through convex space learning Bej, Saptarshi 1991- VerfasserIn aut Wolkenhauer, Olaf 1966- AkademischeR BetreuerIn dgs Baumbach, Jan AkademischeR BetreuerIn dgs Ullrich, Carsten AkademischeR BetreuerIn dgs Universität Rostock Grad-verleihende Institution dgg Universität Rostock Fakultät für Informatik und Elektrotechnik Grad-verleihende Institution dgg text theses Text Hochschulschrift gw 2021 monographic XA-DE Rostock Universität publisher 2021 Rostock Universitätsbibliothek distributor eng 1 Online-Ressource Imbalanced datasets for classification problems, characterised by unequal distribution of samples, are abundant in practical scenarios. Oversampling algorithms generate synthetic data to enrich classification performance for such datasets. In this thesis, I discuss two algorithms LoRAS & ProWRAS, improving on the state-of-the-art as shown through rigorous benchmarking on publicly available datasets. A biological application for detection of rare cell-types from single-cell transcriptomics data is also discussed. The thesis also provides a better theoretical understanding behind oversampling.<eng> vorgelegt von Saptarshi Bej Enthält Poster GutachterInnen: Olaf Wolkenhauer (Universität Rostock) ; Jan Baumbach (Universität Hamburg) ; Carsten Ullrich (Steinbeis Hochschule, CENTOGENE GmbH) Dissertation Universität Rostock 2021 000 004 006.31 004 54.80 http://purl.uni-rostock.de/rosdok/id00003503 https://doi.org/10.18453/rosdok_id00003503 https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00003503-0 https://d-nb.info/1293536814/34 Improved imbalanced classification through convex space learning Rostock, 2021 Druckausgabe (DE-627)1794872353 Bej, Saptarshi, 1991 - urn:nbn:de:gbv:28-rosdok_id00003503-0 10.18453/rosdok_id00003503 1298545513 rda DE-627 220221 1793373833 20251226T201204.0 Converted from MARCXML to MODS version 3.8 using MARC21slim2MODS3-8_XSLT1-0.xsl (Revision 1.174 20250328) ger