A Frequency Based Approach to Multi-Class Text Classification

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Anurag Sarkar 1,* Debabrata Datta 2

1. Northeastern University, Boston, United States

2. St. Xavier’s College (Autonomous), Kolkata, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2017.05.03

Received: 17 Jun. 2016 / Revised: 3 Oct. 2016 / Accepted: 16 Jan. 2017 / Published: 8 May 2017

Index Terms

Supervised learning, Multi-class classification, Text classification, Text mining, Text categorization, tf-idf


Text classification is a method which involves managing and processing important information that can be categorized into predefined classes within a collection of text data. This method plays a vital role in the field of information processing and information retrieval. Different approaches to text classification specifically based on machine learning algorithms have been discussed and proposed in various research works. This paper discusses a classification approach based on the frequencies of some important text parameters and classifies a given text accordingly into one among multiple categories. Using a newly defined parameter called wf-icf, classification accuracy obtained in a previous work was significantly improved upon.

Cite This Paper

Anurag Sarkar, Debabrata Datta, "A Frequency Based Approach to Multi-Class Text Classification", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.5, pp.15-22, 2017. DOI:10.5815/ijitcs.2017.05.03


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