Recent research addresses the problem of data-stream mining
to deal with applications that require processing huge amounts of data
such as sensor data analysis and financial applications.
Data-stream mining algorithms incorporate special provisions to meet
the requirements of stream-management systems, that is stream
algorithms must be online and incremental, processing each data
record only once (or few times); adaptive to distribution changes;
and fast enough to accommodate high arrival rates.
We consider the problem of data-stream classification,
introducing an online and incremental stream-classification
ensemble algorithm, Streaming Random Forests,
an extension of the Random Forests algorithm
by Breiman, which is a standard classification algorithm.
Our algorithm is designed to handle multi-class classification
problems.
It is able to deal with
data streams having an evolving nature and
a random arrival rate of training/test data records.
The algorithm, in addition, automatically adjusts its
parameters based on the data seen so far.
Experimental results on real and synthetic data
demonstrate that the algorithm gives a successful behavior.
Without losing classification accuracy, our algorithm
is able to handle multi-class problems for which the
underlying class boundaries drift, and handle the case when blocks of training
records are not big enough to build/update the classification model. / Thesis (Ph.D, Computing) -- Queen's University, 2008-07-15 16:12:33.221
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/1321 |
| Date | 16 July 2008 |
| Creators | Abdulsalam, Hanady |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | 1183066 bytes, application/pdf |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
Page generated in 0.0019 seconds