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Neural Network Approach for Text Classification using Relevance Factor as Term Weighing Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 68 - Number 17 |
| Year of Publication: 2013 |
| Authors: Anuradha Patra, Divakar Singh |
10.5120/11674-7301
|
Anuradha Patra, Divakar Singh . Neural Network Approach for Text Classification using Relevance Factor as Term Weighing Method. International Journal of Computer Applications. 68, 17 ( April 2013), 37-41. DOI=10.5120/11674-7301
Abstract
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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