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Survey Paper | Computer Science & Engineering | India | Volume 5 Issue 5, May 2016
A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification
Pradnya Kumbhar, Manisha Mali
The rapid growth of World Wide Web has led to explosive growth of information. As most of information is stored in the form of texts, text mining has gained paramount importance. With the high availability of information from diverse sources, the task of automatic categorization of documents has become a vital method for managing, organizing vast amount of information and knowledge discovery. Text classification is the task of assigning predefined categories to documents. The major challenge of text classification is accuracy of classifier and high dimensionality of feature space. These problems can be overcome using Feature Selection. Feature selection is a process of identifying a subset of the most useful features from the original entire set of features. Feature selection (FS) is a strategy that aims at making text document classifiers more efficient and accurate. Feature selection methods provide us a way of reducing computation time, improving prediction performance, and a better understanding of the data. This paper surveys of text classification, several approaches of text classification, feature selection methods and applications of text classifications.
Keywords: Feature Selection, Feature selection methods, Text Classification, Text Classification Algorithms, Text Mining
Edition: Volume 5 Issue 5, May 2016
Pages: 1267 - 1275
How to Cite this Article?
Pradnya Kumbhar, Manisha Mali, "A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=NOV163675, Volume 5 Issue 5, May 2016, 1267 - 1275
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