Efficient Feature Selection and Domain Relevance Term Weighting Method for Document Classification
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...
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| 主要な著者: | , , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
2010
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| 主題: | |
| オンライン･アクセス: | http://eprints.utp.edu.my/6431/1/Efficient_Feature_Selection_and_Domain_Relevance_Term_Weighting_Method_for.pdf http://eprints.utp.edu.my/6431/ |
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| 要約: | 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. |
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