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Full-Text Articles in Physical Sciences and Mathematics
Evaluation Of Supervised Machine Learning For Classifying Video Traffic, Farrell R. Taylor
Evaluation Of Supervised Machine Learning For Classifying Video Traffic, Farrell R. Taylor
CCE Theses and Dissertations
Operational deployment of machine learning based classifiers in real-world networks has become an important area of research to support automated real-time quality of service decisions by Internet service providers (ISPs) and more generally, network administrators. As the Internet has evolved, multimedia applications, such as voice over Internet protocol (VoIP), gaming, and video streaming, have become commonplace. These traffic types are sensitive to network perturbations, e.g. jitter and delay. Automated quality of service (QoS) capabilities offer a degree of relief by prioritizing network traffic without human intervention; however, they rely on the integration of real-time traffic classification to identify applications. Accordingly, …
Using Diversity Ensembles With Time Limits To Handle Concept Drift, Robert M. Van Camp
Using Diversity Ensembles With Time Limits To Handle Concept Drift, Robert M. Van Camp
CCE Theses and Dissertations
While traditional supervised learning focuses on static datasets, an increasing amount of data comes in the form of streams, where data is continuous and typically processed only once. A common problem with data streams is that the underlying concept we are trying to learn can be constantly evolving. This concept drift has been of interest to researchers the last few years and there is a need for improved machine learning algorithms that are capable of dealing with concept drifts. A promising approach involves using an ensemble of a diverse set of classifiers. The constituent classifiers are re-trained when a concept …