relation: https://khub.utp.edu.my/scholars/1244/
title: Efficient feature selection and domain relevance term weighting method for document classification
creator: Khan, A.
creator: Baharudin, B.
creator: Khan, K.
description: 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.
date: 2010
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Khan, A. and Baharudin, B. and Khan, K.  (2010) Efficient feature selection and domain relevance term weighting method for document classification.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952774461&doi=10.1109%2fICCEA.2010.228&partnerID=40&md5=02a06ac08bdb055f2ac9cab1fffa4345
relation: 10.1109/ICCEA.2010.228
identifier: 10.1109/ICCEA.2010.228