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Reducing performance bias by intrinsically insensitive learning for unbalanced text mining

Zhuang, Ling. 2006, Reducing performance bias by intrinsically insensitive learning for unbalanced text mining, Ph.D. thesis, School of Engineering and Information Technology, Deakin University.


Title Reducing performance bias by intrinsically insensitive learning for unbalanced text mining
Author Zhuang, Ling.
Institution Deakin University
School School of Engineering and Information Technology
Faculty Faculty of Science and Technology
Degree name Ph.D.
Date submitted 2006
Keyword(s) Data mining
Summary This thesis proposes three effective strategies to solve the significant performance-bias problem in imbalance text mining: (1) creation of a novel inexact field learning algorithm to overcome the dual-imbalance problem; (2) introduction of the one-class classification-framework to optimize classifier-parameters, and (3) proposal of a maximal-frequent-item-set discovery approach to achieve higher accuracy and efficiency.
Notes Degree conferred 2007.
Language eng
Description of original x, 151 p. : ill. ; 30 cm.
Dewey Decimal Classification 006.312
Persistent URL http://hdl.handle.net/10536/DRO/DU:30027089

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Created: Thu, 01 Apr 2010, 15:52:56 EST

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