Development of an Active Learning Approach for One Class Classifi cation using Bayesian Uncertainty

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Version vom 24. Mai 2022, 11:37 Uhr von Bela Hraban Böhnke (Diskussion | Beiträge)
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Vortragende(r) Tobias Haßberg
Vortragstyp Masterarbeit
Betreuer(in) Bela Böhnke
Termin Fr 3. Juni 2022
Vortragsmodus in Präsenz
Kurzfassung In One-Class classification, the classifier decides if points belong to a specific class. In this thesis, we propose an One-Class classification approach, suitable for active learning, that models for each point, a prediction range in which the model assumes the points state to be. The proposed classifier uses a Gaussian process. We use the Gaussian processes prediction range to derive a certainty measure, that considers the available labeled points for stating its certainty. We compared this approach against baseline classifiers and show the correlation between the classifier's uncertainty and misclassification ratio.