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 11 of 11 for “"Class Imbalance Problem"”.
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A Novel Penalized Log-likelihood Function for Class Imbalance Problem
… is based on assumptions that the target classes are equally distributed and the overall accuracy is maximized, which do not apply to class imbalance problems (e.g., fraud detection, rare disease diagnoses, customer conversion prediction, cybersecurity, predictive maintenance). When …
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Advancing Cross-Domain Fake News Detection: Enhanced Models to Improve Generalization and Tackle the Class Imbalance Problem
… Among these, cross-domain generalization and class imbalance are two critical problems that considerably impact the performance of detection systems. Although there are multiple challenges in FND, this thesis focuses on these two problems due to their widespread influence across various …
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Decision tree rule-based feature selection for imbalanced data
A class imbalance problem appears in many real world applications, e.g., fault diagnosis, text categorization 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 …
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Learning from small and imbalanced dataset of images using generative adversarial neural networks.
… supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which 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 …
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Information filtering by multiple examples
… (SBME), a promising method for overcoming this problem, allows users to specify their information needs as a set of relevant documents rather than as a set of keywords. Most of the studies on SBME adopt the Positive Unlabeled learning (PU learning) techniques by treating the user's provided …
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Risk stratification of cardiovascular patients using a novel classification tree induction algorithm with non-symmetric entropy measures
… stratification models from medical data is the class imbalance problem. Typically the number of patients that experience a serious medical event is a small subset of the entire population. The goal of my thesis work is to develop automated tools to build risk stratification models that can …
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Developing Learning Methods for Non-stationary and Imbalanced Data Streams
… streams generated by real-world applications are imbalanced within themselves. This difficulty is more acute in multi-class learning tasks. Despite "learning from non-stationary streams" and "class imbalance" problems having been investigated separately in the literature, too little attention has …
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A Constrained Box Algorithm for Imbalanced Data in Remote Sensing Images
<p>Imbalanced data is a common problem in machine learning where the number of observations that belong to one class is significantly lower than other classes. Due to the skewed distribution among the classes, most classification algorithms fail to classify minority instances effectively. The class …
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Cross channel fraud detection framework in financial services using recurrent neural networks
… and innovative attacks that deceive banks. The problems are further exacerbated with evolving customer behaviour as existing fraud detection models unable to cope with class imbalance problem and longer feedback loop. This thesis looks at the holistic view of fraud detection and proposes a …
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Clustering to Improve One-Class Classifier Performance in Data Streams
The classification task requires learning a decision boundary between classes by making use of training examples from each. A potential challenge for this task is the class imbalance problem, which occurs when there are many training instances available for a single class, the majority class, and …