| Description |
x, 151 pages : illustrations ; 30 cm |
| 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 |
Submitted to the School of Engineering and Information Technology of the Faculty of Science and Technology, Deakin University |
|
Degree conferred 2007 |
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Thesis (Ph.D.)--Deakin University, Victoria, 2006 |
| Bibliography |
Includes bibliographical references (pages 132-147) and index |
| Subject |
Data mining.
|
| Genre/Form |
Academic theses.
|
| Author |
Deakin University. Faculty of Science and Technology.
|
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Deakin University. School of Engineering and Information Technology
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