The week's pick
Reseach Article
A Classification Framework based on VPRS Boundary Region using Random Forest Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 158 - Number 8 |
| Year of Publication: 2017 |
| Authors: Hemant Kumar Diwakar, Sanjay Keer |
10.5120/ijca2017912842
|
Hemant Kumar Diwakar, Sanjay Keer . A Classification Framework based on VPRS Boundary Region using Random Forest Classifier. International Journal of Computer Applications. 158, 8 ( Jan 2017), 21-26. DOI=10.5120/ijca2017912842
Abstract
Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to computers to achieve optimization .Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which be select more significant attributes in the given datasets. These proposed a novel hybrid approach combination of VPRS with Boundary Region and Random Forest algorithm called VPRS Boundary Region based Random Forest Classifier (VPRSBRRF Classifier) which is used to deal with uncertainties, vagueness and ambiguity associated with datasets. In this approach, select significant attributes based on variable precision rough set theory with boundary region as an input to Random Forest classifier for constructing the decision tree which is more efficient and scalable approach for classification of various datasets.
References
- Nguyen H S,Skowron A. Quantization of real value attributes. Proceedins of Second Joint Annual Conf. on Information Science, Wrightsville Beach,North Carolina,pp34-37,1995
- Z. Pawlak, Rough sets, Int. J. Comput. Inform. Sci. 11 (1982) 341–356.
- Discretization Techniques: A recent survey Sotiris Kotsiantis, Dimitris Kanellopoulos Educational Software Development Laboratory Department of Mathematics, University of Patras, Greece, GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), 2006, pp. 47-58
- Rough Sets, their Extensions and ApplicationsQiang Shen_ Richard Jensen. 04(1), January 2007, 100-106 DOI: 10.1007/s10453-004-5872-7
- W. Ziarko. Variable Precision Rough Set Model. Journal of Computer and System Sciences, vol. 46, no. 1, pp. 39–59, 1993.
- F. Jiang, Z.X. Zhao,Y. Ge, A supervised and multivariate discretization algorithm for rough sets, in: Proc. of the 5th International Conference on Rough Set andKnowledge Technology, LNCS, vol. 6401, 2010, pp. 596–603.
- R. Kerber, Chimerge: discretization of numeric attributes, in: Proc. of the Ninth National Conf. of Articial Intelligence, 1992, pp. 123–128
- Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991
- G.Y. Wang, Rough Set Theory and Knowledge Acquisition, Xian Jiaotong University Press, 2001.
- https://archive.ics.uci.edu/ml/datasets.html
- “A novel approach for discretization of continuous attributes in rough set theory “Feng Jiang a,⇑, Yuefei Sui b College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, PR China b Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, PR China. Knowledge-Based Systems 73 (2015) 324–334
- “Random Forests “Leo Breiman Statistics Department University of California Berkeley, CA 94720 January 2001
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