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Online Ensemble Learning in the Presence of Concept Drift

Research Area:  Machine Learning


In online learning, each training example is processed separately and then discarded. Environments that require online learning are often non-stationary and their underlying distributions may change over time (concept drift). Even though ensembles of learning machines have be ensued for handling concept drift, there has been no deep study of why they can be helpful for dealing with drifts and which of their features can contribute for that. The thesis mainly investigates how ensemble diversity affects accuracy in online learning in the presence of concept drift and how to use diversity in order to improve accuracy in changing environments. This is the first diversity study in the presence of concept drift.

Name of the Researcher:  LEANDRO LEI MINKU

Name of the Supervisor(s):   Xin Yao

Year of Completion:  2010

University:  The University of Birmingham

Thesis Link:   Home Page Url