Classification under streaming data conditions requires that the machine learning approach operate interactively with the stream content. Thus, given some initial machine learning classification capability, it is not possible to assume that the process `generating' stream content will be stationary. It is therefore necessary to first detect when the stream content changes. Only after detecting a change, can classifier retraining be triggered. Current methods for change detection tend to assume an entropy filter approach, where class labels are necessary. In practice, labeling the stream would be extremely expensive. This work proposes an approach in which the behavior of GP individuals is used to detect change without} the use of labels. Only after detecting a change is label information requested. Benchmarking under three computer network traffic analysis scenarios demonstrates that the proposed approach performs at least as well as the filter method, while retaining the advantage of requiring no labels.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/35420 |
| Date | 09 August 2013 |
| Creators | Rahimi, Sara |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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