A Frequency Based Approach to Multi-Class Text Classification
Author(s) -
Anurag Sarkar,
Debabrata Datta
Publication year - 2017
Publication title -
international journal of information technology and computer science
Language(s) - English
Resource type - Journals
eISSN - 2074-9015
pISSN - 2074-9007
DOI - 10.5815/ijitcs.2017.05.03
Subject(s) - computer science , class (philosophy) , field (mathematics) , information retrieval , artificial intelligence , natural language processing , data mining , machine learning , mathematics , pure mathematics
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.
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