Chen, StephenKuo, Te-Chuan2024-10-282024-10-282024-08-012024-10-28https://hdl.handle.net/10315/42385This thesis evaluates a hierarchical classification model applied to the CIFAR-10 dataset, focusing on addressing the limitations of existing methods, which often struggle with (i) overlapping features and (ii) poor interpretability of classification decisions. The hierarchical model was implemented to mitigate these issues by refining classification through a multi-stage process that narrows the focus progressively. Our hierarchical approach has demonstrated its ability to focus on distinguishing features critical to specific classes and groups/pairs. Furthermore, the hierarchical models provide enhanced transparency over the baseline model by allowing a granular examination of classification performance across multiple stages.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyComputer scienceElectronic Thesis or Dissertation2024-10-28Hierarchical classificationImage classificationTrustworthy AIConvolutional neural networksDivide-and-conquer