Improved imbalanced classification through convex space learning Bej, Saptarshi , 1991- (VerfasserIn) Wolkenhauer, Olaf , 1966- (AkademischeR BetreuerIn) Baumbach, Jan (AkademischeR BetreuerIn) Ullrich, Carsten (AkademischeR BetreuerIn) Universität Rostock (Grad-verleihende Institution) Universität Rostock Fakultät für Informatik und Elektrotechnik (Grad-verleihende Institution) Text theses Text Hochschulschrift 2021 2021 eng electronic resourceremoteComputermedienOnline-Ressource1 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--(DE-627)1794872353 urn:nbn:de:gbv:28-rosdok_id00003503-0 doi: 10.18453/rosdok_id00003503 oclc: 1298545513 ppn: (DE-627)1793373833