This thesis proposes an innovative adaptive multi-classifier spam filtering model, with a grey-list analyser and a dynamic feature selection method, to overcome false-positive problems in email classification. It also presents additional techniques to minimize the added complexity. Empirical evidence indicates the success of this model over existing approaches
Notes
Submitted to the School of Engineering and Information Technology of the Faculty of Science and Technology, Deakin University
Thesis (Ph.D.)--Deakin University, Victoria, 2008
Bibliography
Includes bibliographical references (leaves 179-195)
