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Study of K-NN Evaluation for Text Categorization using Multiple Level Learning

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 22
Year of Publication: 2015
Authors:
Monika
Rajender Singh Chhillar
10.5120/21855-5151

Monika and Rajender Singh Chhillar. Article: Study of K-NN Evaluation for Text Categorization using Multiple Level Learning. International Journal of Computer Applications 122(22):9-12, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Monika and Rajender Singh Chhillar},
	title = {Article: Study of K-NN Evaluation for Text Categorization using Multiple Level Learning},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {22},
	pages = {9-12},
	month = {July},
	note = {Full text available}
}

Abstract

Predefined category exists for text categorization. In a document, text may be of any type category like government, education or health etc. many methods exist in market invented by researchers for text categorization. One of them is k-NN (k nearest neighbor) algorithm. k play a role to define number of classes for categorization. A training set is generated for each type of category to check its performance than whole text categorized. There is a problem of missing information during training sets. After study recent years invention on k-NN, we find out a solution of this problem. Multiple-Level Learning will improve the performance of k-NN. So in this paper we study about k-NN and propose hybrid algorithm with combination of Multiple-Level Learning and k-NN.

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