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Reducing performance bias by intrinsically insensitive learning for unbalanced text mining

thesis
posted on 2006-01-01, 00:00 authored by Ling. Zhuang
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.

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Pagination

x, 151 p. : ill. ; 30 cm.

Material type

thesis

Resource type

thesis

Language

eng

Notes

Degree conferred 2007.

Degree name

Ph.D.

Faculty

Faculty of Science

School

Engineering and Built Environment

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