M.  Santhanakumar, C.  Christopher Columbus, K.  Jayapriya, 
    Frequency Based Modified Term Weighting Method for Text Classification,
    Asian Journal of Information Technology,
    Volume 15,Issue 18,
    2016,
    Pages 3430-3440,
    ISSN 1682-3915,
    ajit.2016.3430.3440,
    (https://makhillpublications.co/view-article.php?doi=ajit.2016.3430.3440)
    Abstract: 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.
    Keywords: Term frequency;inverse document frequency;term weighting;classification;India