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An Optimized Classifier Frame Work based on Rough Set and Random Tree

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Nidhi Patel
10.5120/ijca2017912844

Nidhi Patel. An Optimized Classifier Frame Work based on Rough Set and Random Tree. International Journal of Computer Applications 160(9):1-7, February 2017. BibTeX

@article{10.5120/ijca2017912844,
	author = {Nidhi Patel},
	title = {An Optimized Classifier Frame Work based on Rough Set and Random Tree},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {9},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume160/number9/27098-2017912844},
	doi = {10.5120/ijca2017912844},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Over the past two decades, Machine Learning has become one of the mainstays of information technology. Machine learning is concerned with the development of algorithms and achieves optimization classification of attributes. Classification under the decision tree is the prediction approach of data mining techniques. In the decision tree, classification algorithm has the most common classifier to build tree. This research work proposes an optimized classifier framework based on rough set and random tree classifier. Therefore, this paper puts forward a new algorithm, which combined with rough set theory and random Tree, here rough set theory used to reduce the attributes in the decision system, and uses the reduct data, as the input of decision tree. Random tree algorithm is increase high accuracy rate of the result. This article has put new concepts into practice, and the result of these concepts shows that rough set with random tree classifier have high accuracy and low time consumption compared over the rough set based J48 classifier.

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Keywords

Data mining; Rough Set; Reduce Attributes; Decision Tree; Random Tree; MATLAB;WEKA