Reducing performance bias by intrinsically insensitive learning for unbalanced text mining
Data(s) |
01/01/2006
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Deakin University, Faculty of Science and Technology, School of Engineering and Information Technology |
Palavras-Chave | #Data mining |
Tipo |
Thesis |