期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2017
卷号：7
期号：1
页码：324-329
DOI：10.11591/ijece.v7i1.pp324-329
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：In each and every field of science and technology Information science plays an important role. Sometimes information science is facing different types of problems to handle the data and information. Data Uncertainty is one of the challenging difficulties to handle. In past, there are several theories like fuzzy set, Rough set, Probability etc.to dealing with uncertainty. Soft set theory is the youngest theory to deal with uncertainty. In this paper we discussed how to find reducts. This paper focuses on how we can transform a sample data set to binary valued information system. We are also going to reduce the dimension of data set by using the binary valued information that results a better decision.
其他摘要：In each and every field of science and technology Information science plays an important role. Sometimes information science is facing different types of problems to handle the data and information. Data Uncertainty is one of the challenging difficulties to handle. In past, there are several theories like fuzzy set, Rough set, Probability etc.to dealing with uncertainty. Soft set theory is the youngest theory to deal with uncertainty. In this paper we discussed how to find reducts. This paper focuses on how we can transform a sample data set to binary valued information system. We are also going to reduce the dimension of data set by using the binary valued information that results a better decision.
关键词：Machine Learning;approximation; data uncertainty; information system; parameterization; reduct; soft set predicate; softset theory
