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 20 of 46 for “"Concept Drift"”.
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Unsupervised Concept Drift Detection in Data Streams
In data 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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Towards handling temporal dependence in concept drift streams.
… the problematic and prevalent issue of concept drift has produced a considerable number of methods that allow online classifiers to adapt to changes in the stream distribution. However, recent research suggests that the presence of temporal dependence can cause misleading evaluation when …
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Online ensemble learning in the presence of concept drift
… underlying distributions may change over time (concept drift). Even though ensembles of learning machines have been used for handling concept drift, there has been no deep study of why they can be helpful for dealing with drifts and which of their features can contribute for that. The thesis …
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Handling Concept Drift Using the Correlation between Multiple Data Streams
Concept Drift has been a major issue in handing streaming data in machine learning area. To date, the research on concept drift considers data streams separately, ignoring the correlations between data streams. Motivated by this, this research proposes four methods to deal with the correlations …
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A context-aware approach for handling concept drift in classification
… In particular, the description of the target concept is not static and may change over time under the influence of varying environmental conditions (i.e. varying context). Although many adaptive learning approaches have been proposed in the literature to address such changes, these are limited …
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Measuring concept drift in malware and network intrusion detection models
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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New probabilistic approaches for detecting and evaluating concept drift in data streams
… data to underperform. This phenomenon, known as Concept Drift (CD), presents a major challenge in adaptive learning environments, necessitating ongoing monitoring and adjustment to accommodate evolving data streams. Active drift detection methods, which track changes in data distribution or model …
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Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection
… a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes …
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Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection
… a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes …
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Temporal Data Mining in a Dynamic Feature Space
… for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject …
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A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams
… of (distributional) 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 …
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Novel machine learning approaches for modeling variations in semiconductor manufacturing
… developed. Challenges include class imbalance, concept drift (temporal variation) and feature selection. Batch and online learning methods are introduced to overcome the class imbalance. Incremental learning frameworks are developed to handle concept drift and class imbalance simultaneously. We …
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Active learning for data streams.
… unlabelled data. In this thesis, we borrow the concept of SSL by allowing AL algorithms to make use of redundant unlabelled data so that both labelled and unlabelled data are used in their querying criteria. Another common tradition within the AL community is to assume that data samples are …
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Learning with high dimensional data and preprocessing in non-stationary environments
… analysis. These issues include the adaption to Concept Drift, which is a shift in the data distribution and needs to be handled by classification algorithms. Furthermore, real world data is often high dimensional, while current research focuses mainly on low dimensional problems which do not …
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Bridging the gap: Applying machine learning techniques in digital forensics
… is 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 …
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Learning in non-stationary Environments
… called 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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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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Improving ensembles and prediction intervals for machine learning on data streams
… dynamic methods to address key issues, including concept drift, uncertainty quantification, and ensemble optimization, in evolving data streams. The Self-Optimising K Nearest Leaves (SOKNL) regression algorithm integrates k-Nearest Neighbors (kNN) and Adaptive Random Forest Regression (ARF-Reg), …
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Evaluation, Interpretation, and Maintenance of Machine Learning Models for IT Operations
… decisions to understand the data leakage and concept drift challenges in the model evaluation stage. Our findings motivate practitioners to take precautions against using the random data splitting method that could induce data leakage. We assess the factors that impact the consistency of AIOps …
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