Decision boundary setting and classifier combination for text classification
Bijaksana, Moch Arif (2015) Decision boundary setting and classifier combination for text classification. PhD thesis, Queensland University of Technology.
This thesis presents a promising boundary setting method for solving challenging issues in text classification to produce an effective text classifier. A classifier must identify boundary between classes optimally. However, after the features are selected, the boundary is still unclear with regard to mixed positive and negative documents. A classifier combination method to boost effectiveness of the classification model is also presented. The experiments carried out in the study demonstrate that the proposed classifier is promising.
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Li, Yuefeng & Sitbon, Laurianne|
|Keywords:||Text Classification, Decision Boundary Setting, Pattern Mining, Relevance Feature Discovery, Classifier Combination|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Institution:||Queensland University of Technology|
|Deposited On:||13 May 2015 04:25|
|Last Modified:||22 Mar 2016 00:20|
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