TY  - JOUR
T1  - Frequency Based Modified Term Weighting Method for Text Classification
AU - Santhanakumar, M. AU - Christopher Columbus, C. AU - Jayapriya, K. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 18
SP  - 3430
EP  - 3440
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3430.3440
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3430.3440
KW  - Term frequency
KW  -inverse document frequency
KW  -term weighting
KW  -classification
KW  -India
AB  - Due to the huge amount of data on the World Wide Web (WWW), it is very important that the users
can access the related details without losing any valuable information. Term weighting based on the user query
plays a vital role in Information Retrieval (IR). Term Frequency-Inverse Document Frequency (TF-IDF) is one
of the repeatedly used term weighting method which assigns weights based on the occurrences of a term in a
document. This paper proposes a Modified Term Frequency (MTF) using multi term occurrences in a document.
In the proposed work, the weight is assigned to the documents based on the occurrences of the co-terms in
a document and it is classified to find the accuracy using three different classifiers such as Support Vector
Machine (SVM), Decision Tree (DT) and K- Nearest Neighbor (KNN). The experimental result shows that the
classification accuracy and other performance measures such as precision, recall and f-measure of the propose
work outperforms the some of the existing other term weighting methods.
ER  -