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Efficient Feature Selection and Domain Relevance Term Weighting Method for Document Classification

Aurangzeb , khan and Baharum, Baharudin and Khairullah, khan (2010) Efficient Feature Selection and Domain Relevance Term Weighting Method for Document Classification. In: 2010 Second International Conference on Computer Engineering and Applications.

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Abstract

Feature selection is of paramount concern in document classification process which improves the efficiency and accuracy of text classifier. Vector Space Model is used to represent the “Bag of Word” BOW of the documents with term weighting phenomena. Documents representing through this model has some limitations that is, ignoring term dependencies, structure and ordering of the terms in documents. To overcome this problem semantic base feature vector is proposed. That is used to extracts the concept of term, co-occurring and associated terms using ontology. The proposed method is applied on small documents dataset, which shows that this method outperforms then term frequency/ inverse document frequency (TF-IDF) with BOW feature selection method for text classification.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > T Technology (General)
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:6431
Deposited By: Dr Baharum Baharudin
Deposited On:26 Sep 2011 09:36
Last Modified:19 Jan 2017 08:24

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