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.
Results
Showing 1 to 8 of 8 for “"Concept Drift Detection"”.
-
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 …
-
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 …
-
Um framework semissupervisionado para classificação de dados em fluxos contínuos
… an ensemble 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 …
-
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 …
-
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 …
-
Meta-level learning for the effective reduction of model search space.
… 3) adaptivity mechanism parameters, 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 …
-
On robust and adaptive soft sensors.
… the sources of these obstacles and proposing a concept for dealing with them is the general purpose of this work. The proposed solution addressing the issues of current soft sensors is a conceptual architecture for the development of robust and adaptive soft sensing algorithms. The architecture …
-
Enhancing Robustness and Interpretability in Computer Vision AI
L'abstract è presente nell'allegato / the abstract is in the attachment