Survey on Feature Selection for Text Categorization

Authors(2) :-Sonali Suskar, Dr. S. D. Babar

In this massive amount of data, data is too vast so that text categorization is important issue. With the help of previously organize set of documents and classes we can automatically classify data. The filter approach is predominantly used in text categorization because of its simplicity and efficiency. However, the filter approach evaluates the goodness of a feature by only exploiting the intrinsic characteristics of the training data without considering the learning algorithm for discrimination, which may lead to an undesired classification performance. Given a specific learning algorithm, it is hard to determine which filter feature selection approach is the best for discrimination. This survey mainly focuses on the techniques used for feature selection method used for text categorization. This survey also presents the comparative analysis of such recent techniques along with their limitations.

Authors and Affiliations

Sonali Suskar
Department of Computer Engineering, SIT College of Engineering, Lonavala, Maharashtra, India
Dr. S. D. Babar
Department of Computer Engineering, SIT College of Engineering, Lonavala, Maharashtra, India

Classification, text categorization, feature selection, training data.

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Publication Details

Published in : Volume 3 | Issue 4 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 261-266
Manuscript Number : CSEIT1833255
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sonali Suskar, Dr. S. D. Babar, "Survey on Feature Selection for Text Categorization ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.261-266, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833255

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