Asian Journal of Computer Science and Technology (AJCST)
An Experimental Analysis on Rough Set Mean, Median, Mode Method of Dependency Values for Feature Selection in Medical Databases
Author : S. Devi and V. SasirekhaVolume 8 No.1 Special Issue:February 2019 pp 103-106
Abstract
The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became an important issue for scientists, particularly in the area of Artificial Intelligence. Their square measure several approaches to the matter of the way to perceive and manipulate imperfect information. The most successful approach is based on the rough set notion proposed by Z. Pawlak in the article [1]. The proposed method to find the quick reduct in medical data set using the roughest theory. This method has applied in many classification algorithms and find the measures to calculate the accuracy of this proposed method.
Keywords
Rough Set, Dependency Values, Approximation, RST Mean, RST Median, RST Mode
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References
[1] Z. Pawlak, “Rough set theory and its applications to data analysis”, Cybernetics and Systems, pp.661-688, 29, Oct. 2010.
[2] C. Wang, M. Shao, Q. He, Y. Qian and Y. Qi, “Feature subset selection based on fuzzy neighborhood rough sets”- Knowledge-based systems, Elsevier, Vol. 111, pp. 173-179, Nov. 2016.
[3] N. Zhong, J. Dong and S. Ohsuga “Using rough sets with heuristics for feature selection”, Journal of intelligent information systems, Springer Vol. 16, No. 3, pp. 199-214, Aug. 2001.
[4] M. Modrzejewski, “Feature selection using rough sets theory”, in Proc. ECML1993, Springer, pp. 216-226, April 1993.
[5] M. S. Raza and U. Qamar, “An incremental dependency calculation technique for feature selection using rough sets”, Information Sciences, Elsevier, Vol. 344, pp. 41-65, May 2016.
[6] Y. Qian, J. Liang, W. Pedrycz and C. Dang ,” Positive approximation: an accelerator for attribute reduction in rough set theory”, Artificial Intelligence, Elsevier, Vol. 174, No. 9-10, pp. 597-618, June 2010
[7] Kuo, Tien-Fang, “Approximate Reducts of an Information System,” in Proc. RSFDGrC 2003, Springer, pp. 291-294, May 2003.
[8] J. Dai and Q. Xu, “Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification”, Applied Soft Computing, Elsevier, Vol. 13, No. 1, Jan2013.
[9] H. H. Inbarani and S. S. Kumar, “Soft rough sets for heart valve disease diagnosis”, in Proc. AMLTA 2014, Springer, pp. 347-356, 2014.
[10] Y. Yao and Y. Zhao, “Attribute reduction in decision-theoretic rough set models”, Information sciences, Elsevier, Vol. 178, No. 17, pp. 3356-3373, Sep. 2008.