Computing and Information Systems - Research Publications
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ItemNo Preview AvailableAlgorithmic Decisions, Desire for Control, and the Preference for Human Review over Algorithmic Review(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableLossy Compression Options for Dense Index Retention(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableKey Considerations for The Design of Technology for Enrichment in Residential Aged Care: An Ethnographic Study(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableThe Future Can’t Help Fix The Past: Assessing Program Repair In The Wild(IEEE, 2023-10-01)
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ItemNo Preview AvailableA Group Formation Game for Local Anomaly Detection(IEEE, 2023-01-01)
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ItemIdentification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning(SCITEPRESS - Science and Technology Publications, 2024)Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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ItemUncertainty in Selective Bagging: A Dynamic Bi-objective Optimization Model(SIAM, 2023)Bagging is a common approach in ensemble learning that generates a group of classifiers through bootstrapping for classification tasks. Despite its wide applications, generating redundant classifiers remains a central challenge in bagging. In recent years, many selective bagging models have been presented to deal with this challenge. These models mostly focused on the accuracy of classifiers and the diversity among them. Despite the importance of uncertainty in the performance of ensemble classifiers, this criterion has been neglected in selective bagging models. In this paper, we propose a two-stage selective bagging model. In the first stage, we formalize the selective bagging problem as a bi-objective optimization model considering both the uncertainty and accuracy of classifiers. We propose an adaptive evolutionary Two-Arch2 algorithm, named Diverse-Two-Arch2, to solve the bi-objective model. The output of this stage is a subset of classifiers that are diverse, certain about correct predictions, and uncertain about incorrect predictions. While most selective bagging models focus on the selection of a fixed subset of classifiers for all test samples (static approach), our proposed model has a dynamic approach to the selection process. So, in the second stage of the model, we select only certain classifiers to make an ensemble prediction for each test sample. Experimental results on twenty data sets and comparing with two ensemble models, and five state-of-the-art dynamic selective bagging models show the outperformance of the proposed model. We also compare the performance of the proposed Diverse-Two-Arch2 to alternative evolutionary computation methods.