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Random Forest Classifier based on Variable Precision Rough Set Theory

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Subi Jain, Gagan Vishwakarma, Yogendra Kumar Jain
10.5120/ijca2017914866

Subi Jain, Gagan Vishwakarma and Yogendra Kumar Jain. Random Forest Classifier based on Variable Precision Rough Set Theory. International Journal of Computer Applications 169(9):1-5, July 2017. BibTeX

@article{10.5120/ijca2017914866,
	author = {Subi Jain and Gagan Vishwakarma and Yogendra Kumar Jain},
	title = {Random Forest Classifier based on Variable Precision Rough Set Theory},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {9},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume169/number9/28010-2017914866},
	doi = {10.5120/ijca2017914866},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Decision-making process is supported by Machine learning-based classification techniques in many areas of health care. Classification performance of decision system can be improved using the attribute reduction mainly in the situation of high data dimensionality dilemma .This paper proposes, Random forest Classifier (RFC) approach which is based on the Variable Precision Rough Set (VPRS) theory. The first phase of proposed approach focus at attribute reduction of available dataset using VPRS .Directing from dimensionality reduction to predictive model construction, and in next phase, the obtained abridged dataset is provided as the input of RFC to build a more accurate classification model. The performance is evaluated in terms of classification accuracy and time complexity. The experimental results show that the enhanced RFC has higher accuracy and correctly classified instances as compared with the existing algorithms.

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Keywords

Decision tree classification, Attribute reduction, Variable Precision Rough Set (VPRS), Random forest classifier (RFC).