Streaming Random Forests

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.

Department dc:contributor.department

Computing

Year dc:date.issued

2008

Author dc:creator

• Abdulsalam, Hanady

Advisors dc:contributor.supervisor

• Martin, Patrick
• Skillicorn, B. David

• Data mining
• Streams
• Classification algorithms
• Streaming algorithms

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1974/1321

OAI identifier oai:identifier

oai:queensu.scholaris.ca:1974/1321