Reducing performance bias by intrinsically insensitive learning for unbalanced text mining
thesis
posted on 2006-01-01, 00:00authored byLing. 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.