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.
M. Santhanakumar, C. Christopher Columbus and K. Jayapriya, 2016. Frequency Based Modified Term Weighting Method for Text Classification. Asian Journal of Information Technology, 15: 3430-3440.