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 14 of 14 for “"One-class Classification"”.
-
N-SLOPE: A One-Class Classification Ensemble for Nuclear Forensics
<p>One-class classification is a specialized form of classification from the field of machine learning. Traditional classification attempts to assign unknowns to known classes, but cannot handle novel unknowns that do not belong to any of the known classes. One-class classification seeks to …
-
One-class classification in the presence of point, collective, and contextual anomalies
… valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are even the prime objects of interest and need to be exposed early in order to avoid loss, e.g. in credit card fraud detection. One-class classification …
-
Continuous typist verification using machine learning
… identification and extraction, and sample classification. A dataset has been collected that is comparable in size, timing accuracy and content to others in the field, with one important exception: it is derived from real emails, rather than samples collected in an artificial setting. This …
-
Learning in Dynamic Data-Streams with a Scarcity of Labels
… the very realistic situation in which the true class label of an incoming point is not immediately available (or will never be available) or in situations where manually labelling incoming points is prohibitively expensive. The goal of this thesis is to evaluate unsupervised learning as the …
-
Anomaly detection methods for unmanned underwater vehicle performance data
… mission. In this thesis, we consider existing one-class classification anomaly detection techniques since labeled training data from the anomalous class is not readily available. Specifically, we focus on two anomaly detection techniques: (1) Kernel Density Estimation (KDE) Anomaly Detection …
-
Anomalous Motion Detection of Vehicles on Highway using Deep Learning
… Despite the challenges of learning from a single class of data in an unsupervised learning paradigm, previous research shows promising results in detecting spatial as well as temporal anomalies in crowded environments. The advent of self-driving cars provides an opportunity to apply visual anomaly …
-
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 …
-
Graph-based Approach for Anomaly Detection in Video Surveillance
… part presents an offline framework that uses a one-class classifier to model normal activities represented by spatiotemporal features and features extracted using the graph-based approach. In the third part, we propose to learn regularities using autoencoders. This approach is based on the …
-
Autoencoder-based multivariate time series anomaly detection and clustering for diagnosis of mechatronic systems
… analyzed. The Autoencoder approach follows the one-class classification paradigm and is trained unsupervised by reconstructing its own input using fault-free data only. For quantification of the faults or deterioration of the system, a health index is computed from an anomaly score, which is …
-
Image segmentation and object classification for automatic detection of tuberculosis in sputum smears
… segmentation methods were compared and object classification was implemented using various two-class classifiers, for images obtained using a microscope with 100x objective lens magnification. The bacillus identification route established for the 100x images, was applied to images obtained …
-
Anomalous behaviour detection for cyber defence in modern industrial control systems
… from 0.5% to 89% of the dataset. Further, a one-class classification model is developed, leveraging stream rebalancing followed by adaptive machine learning algorithms and drift detection methods. The model is experimentally validated producing promising results including an overall Matthews …
-
Computational statistical methods in chemical engineering
… parametric model for optical spectra of multicomponent mixtures is introduced. The application of interest is the quantification of uncertainty associated with estimating the relative abundance of mixtures of carbon nanotubes in solution. This work describes a detailed analysis of sources of …
-
Machine learning for the prediction of protein-protein interactions
… This task has been commonly viewed as a binary classification problem. However, the nature of the data creates two major problems. Firstly, the imbalanced class problem due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative …
-
Mining specifications in a new world: using a test generator through a learning lens
This Dissertation was approved for publication on 2024-12-06 at 10:26.