LE3 .A278 2006

2006

Chipman, Hugh

Acadia University

Master of Science

Masters

Mathematics and Statistics

Mathematics & Statistics

Of primary interest in this thesis is the general supervised learning problem of studying communication patterns and classifying data on graph networks. Consider the nodes on a graph as communicators, and edges on this graph as messages sent/received between unique nodes. We describe this graph data using summarized features derived from our graph for each node. We then consider 2-class classification, using our features to determine whether or not a node belongs to some class of interest. We consider several sub-problems, including feature selection, and “unbalanced†2-class classification, in which one rare class is of special interest. A concrete example using e-mails related to the Enron scandal will be used to highlight these techniques. Our results show that a handful of features is enough to provide adequate models and that techniques such as random forests and boosted trees prove to be good classifiers, as measured by misclassification rate. A second dataset corresponding to the unbalanced case is also considered. Again, a small number of features could be used to produce accurate predictions, as measured by the area under a lift curve.

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https://scholar.acadiau.ca/islandora/object/theses:115