TY  - CONF
AV  - none
KW  - Bag of words; Data sets; Document Classification; Efficient feature selections; Feature selection; Feature selection methods; Feature vector; Feature vectors; Inverse Document Frequency; Term dependency; Term weighting; Text classification; Text classifiers; Vector space models
KW  -  Classification (of information); Information retrieval systems; Ontology; Text processing; Vector spaces; Vectors
KW  -  Feature extraction
ID  - scholars1244
TI  - Efficient feature selection and domain relevance term weighting method for document classification
SP  - 398
N1  - cited By 11; Conference of 2nd International Conference on Computer Engineering and Applications, ICCEA 2010 ; Conference Date: 19 March 2010 Through 21 March 2010; Conference Code:80409
N2  - 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. © 2010 Crown Copyright.
SN  - 9780769539829
Y1  - 2010///
VL  - 2
EP  - 403
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952774461&doi=10.1109%2fICCEA.2010.228&partnerID=40&md5=02a06ac08bdb055f2ac9cab1fffa4345
A1  - Khan, A.
A1  - Baharudin, B.
A1  - Khan, K.
ER  -