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 12 of 12 for “"Imbalanced dataset"”.
-
Learning from small and imbalanced dataset of images using generative adversarial neural networks.
… are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and …
-
Extensive Huffman-tree-based neural network for the imbalanced dataset and its application in accent recognition
… data-set featured with a large number of heavily imbalanced classes, this thesis proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in EHTNN can have …
-
Decision tree rule-based feature selection for imbalanced data
… and fraud detection. When dealing with an imbalanced dataset, feature selection becomes an important issue. To address it, this work proposes a feature selection method that is based on a decision tree rule and weighted Gini index. The effectiveness of the proposed methods is verified by …
-
Predicting Progression of Metabolic Dysfunction-associated Steatotic Liver Disease
… sensitivity, indicating both overfitting and an imbalanced dataset.
-
Integrating Gradient Boosting and Generative Models: Hybrid Approach to Address Class Imbalance and Evaluation Gaps in Real-World Systems
… efficiency and predictive accuracy—on an imbalanced dataset, comparing its performance against standard academic evaluation criteria. Our results demonstrate that Tail-end FPR Max Recall fills critical gaps left by standard academic criteria, providing a more realistic assessment of model …
-
USING DCGAN TO GENERATE SYNTHETIC PACKET FLOWS FOR THREAT DETECTION
… detection. Such traffic is scarce and often imbalanced as the labeling is intensive and requires domain expertise. Deep Convolutional Generative Adversarial Networks (DCGAN) are known for their image recognition and generation capabilities by learning inherent features within the image. In …
-
Recommending TEE-based Functions Using a Deep Learning Model
… GitHub repositories that use Intel SGX and on an imbalanced dataset. The accuracy of the final model used in the recommendation system has an accuracy of 98.86% and an F1 score of 80.00%. In addition, we conducted a pilot study, in which participants were asked to identify functions that needed to …
-
Modelling highly imbalanced credit card fraud detection data using statistical learning
… face several challenges when dealing with highly imbalanced data, which is often the case with fraud detection. Whilst different sampling techniques are generally used to reduce the imbalance, minimal studies have focussed on the effect the level imbalance has on the predictive capabilities of …
-
Financial and Analytic Innovations for Therapeutic Development
… in the machine learning models trained on the imbalanced dataset of historical drug development outcomes. We also show that debiasing the machine learning model improves the prediction accuracy and generates financial value for the drug developer. Finally, in Part V, we analyze two social and …
-
Time-to-Event Prediction Using Deep Learning Models: Application to GPU Failure Data
… covers embedding layers and the use of the GPU dataset. Finally, it introduces multitask output for neural networks, which will be utilized in Chapter~ref{cha:NNspatialRE}. Chapter~ref{cha:DL} introduces a deep learning approach for predicting GPU failure time and status. We propose two distinct …
-
Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation.
… experimental work on DEMETRA data. The DEMETRA datasets have been produced by the EC-funded project DEMETRA. Based on the investigation, experiments and the results obtained, the author identified a number of data quality criteria in order to provide a solution for data evaluation in toxicology …
-
Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation
… experimental work on DEMETRA data. The DEMETRA datasets have been produced by the EC-funded project DEMETRA. Based on the investigation, experiments and the results obtained, the author identified a number of data quality criteria in order to provide a solution for data evaluation in toxicology …