Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"Drift Detection"”.
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Unsupervised Concept Drift Detection in Data Streams
… stream mining, efficiently detecting concept drifts is still challenging due to the high cost of collecting true class labels. Traditional detection methods usually need high computation and memory cost and is unable to distinguish between concept drift and novelty. To improve the drift …
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A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams
… change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, …
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The Design of GLR Control Charts for Process Monitoring
… special cause may produce a time varying linear drift in the mean. The design and application of a GLR control chart for drift detection is investigated. The GLR drift chart does not require specification of any tuning parameters by the practitioner, and has the advantage that, at the time of the …
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Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data
… environments with few labelled data samples and drifting data features poses a severe challenge. In this thesis, we will address two main technical challenges in the field of analyzing industrial streaming data: (1) how to efficiently train models with only partially labeled data, and (2) how to …
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Um framework semissupervisionado para classificação de dados em fluxos contínuos
… of classifiers can assist in the concept drift detection. So, in this work, we proposed a framework to perform the semi-supervised classification in tasks in a data stream context, using an approach based on an ensemble of classifiers. This framework use an ensemble to evaluate itself and …
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Item parameter drift and online calibration
… and this phenomenon is termed item parameter drift. Several methods have been developed to detect drifted items, and most of the them were designed to detect drifts in the unidimensional item response model under the paper and pencil testing framework, which may not be adequate for …
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An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning
… forgetting prior knowledge, and change/drift detection. The framework is evaluated on a variety of canonical and real-world data streams (weather prediction, electricity price prediction, and spam detection). This study reveals the catastrophic effect of forgetting prior knowledge, …
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A context-aware approach for handling concept drift in classification
… mostly rely on monitoring the effects of drift (in terms of the degradation of the classifier’s performance). Given this, to achieve more effective concept drift management, we propose incorporating context awareness when adapting the classification model to changes. Explicit …
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Bridging the gap: Applying machine learning techniques in digital forensics
… constantly evolving, we also investigate concept drift — a phenomenon where input data distribution changes affect predictive models’ performance. To mitigate the degradation in performance due to concept drift, we introduce a concept drift detection algorithm complemented by a custom sampling …
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Learning in non-stationary Environments
… data stream, is additionally affected by Concept Drift. Domain Adaptation methods are not practical for such scenarios because they do not scale to data stream size. The research area of Concept Drift Stream Classification addresses the challenges just described. It provides a variety of …
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Anomalous behaviour detection for cyber defence in modern industrial control systems
… a novel super learner ensemble anomaly detection and cyber risk quantification framework to profile anomalous behaviour in ICS and derive a cyber risk score. The proposed framework and associated learning models are experimentally validated. The produced results are promising and achieve …
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New probabilistic approaches for detecting and evaluating concept drift in data streams
… financial forecasting, and real-time fraud detection, data distributions frequently shift, causing predictive models trained on historical data to underperform. This phenomenon, known as Concept Drift (CD), presents a major challenge in adaptive learning environments, necessitating ongoing …
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Towards Data Efficiency and Controllable Representations for Deep Learning in Resource-Constrained Domains
… by up to 90% in the case of boundary crossing detection at Mercury’s magnetosphere. To further improve sampling diversity, a GAN-based concept drift detection method is integrated into the DAL framework, leveraging uncertainty and diversity together to offer a sampling method that outperforms …
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Meta-level learning for the effective reduction of model search space.
… 4) recurring concept extraction, and 5) concept drift detection. The scope of this research is limited to feature engineering for problem representation, and learning strategy for algorithm and its hyper-parameters recommendation at Meta-level. There are three studies conducted around the two …
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On robust and adaptive soft sensors.
… learning; (iv) meta-learning; and (v) concept drift detection and handling. The proposed architecture arranges the above techniques into a three-level hierarchy, where the actual prediction-making models operate at the bottom level. Their predictions are flexibly merged by applying ensemble …
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Enhancing Robustness and Interpretability in Computer Vision AI
L'abstract è presente nell'allegato / the abstract is in the attachment