Computing and Information Systems - Theses
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ItemCapturing Uncertainty in Ensemble Models For Human-Machine Collaboration( 2023-08)This thesis studies capturing uncertainty in ensemble models, starting with machine only models and then leading to human-machine collaboration from new perspectives. We have identified two research gaps in the previous studies on capturing uncertainty in ensemble models. First, in machine decision making, while several studies have been presented to introduce combining approaches to deal with inter-source uncertainty in ensemble models, capturing intra-source uncertainty needs to be addressed. Second, the collaboration of humans with machines in ensemble models introduces a new challenge of integrating human uncertainty within these models. Furthermore, when addressing real-world decision making problems through human-machine collaboration, dedicated research efforts are required to investigate this collaborative approach thoroughly. So, each case study in human-machine collaboration for ensemble models requires new approaches. This thesis delves into the first research gap in the context of ensemble classifiers. It studies this research gap in two settings. First, an interval modelling to combine classifiers in a category of ensemble models to capture inter-source and intra-source uncertainties is developed. Through the proposed model, the performance of the ensemble model can be improved by capturing uncertainty in complicated binary classification problems. Second, the ensemble selection problem for bagging as one of the widely used ensemble models in the literature is studied. While consideration of intra-source uncertainty as a selection criterion for classifiers in an ensemble has been previously overlooked, we show its substantial to enhance the performance of the selected ensemble. This study formulates the ensemble selection problem as a bi-objective optimisation problem for bagging and presents an adaptive meta-heuristic algorithm to solve the bi-objective problem. The findings highlight the significance of incorporating intra-source uncertainty into the classifier selection process, leading to improved ensemble model performance. Then we touch upon the second research gap on human-machine collaboration in ensemble models. Specifically, interval modelling approaches to capture uncertainty in ensemble models where both humans and machines make decisions are presented. Through two case studies, we show how this collaboration and interval modelling enhance ensemble performance, by capturing uncertainties from both humans and machines. In the first case study, synthetic data is used to show how human-machine collaboration and capturing the uncertainty of humans and machines can be conducted throughout the ensemble decision making. In the second case study, a real image dataset related to biofouling assessment is utilised to investigate the importance of human-machine collaboration in ensemble models for biofouling detection. Furthermore, interval modelling to examine how capturing uncertainty affects ensemble performance is employed for biofouling detection. In summary, as we explained, this thesis advances the current state of ensemble models by presenting effective interval modelling techniques to capture uncertainty and investigates human-machine collaboration within these models. These contributions aim to enhance ensemble performance, a critical step before implementing these models to deal with real-world decision making problems.