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Neural Network Approach for Text Classification using Relevance Factor as Term Weighing Method

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 17
Year of Publication: 2013
Anuradha Patra
Divakar Singh

Anuradha Patra and Divakar Singh. Article: Neural Network Approach for Text Classification using Relevance Factor as Term Weighing Method. International Journal of Computer Applications 68(17):37-41, April 2013. Full text available. BibTeX

	author = {Anuradha Patra and Divakar Singh},
	title = {Article: Neural Network Approach for Text Classification using Relevance Factor as Term Weighing Method},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {17},
	pages = {37-41},
	month = {April},
	note = {Full text available}


With the rapid growth of online information there is growing need for tools that help in finding filtering and managing the high dimensional data . text classification is a supervised learning task whose goal is to classify document into the predefined categories. Phases involved in text classification are collecting data set, preprocessing, stemming, and implementing the classifier and performance measure. There are several learning method for Text classification such as Naïve bayes, k-nearest neighbor decision tree, SVM, BPNN etc. algorithm is applied to multilayer feed forward networks consisting of processing element with continuous differentiable activation function. The network associated with back propagation learning algorithm called BPNN. This paper demonstrates the result of text classification using BPNN and relevance factor (rf) as term weighing method.


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