Reducing Fuzzy Data Set Attributes in Industrial Internet of Things (IIoT) Using Rough Set Theory

Abstract

Due to the problem of attribute redundancy of fuzzy data set in the Industrial Internet of Things (IIoT) to simplify the induced decision rules without reducing the classification accuracy. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Based on fuzzy rough technique and using an efficient criterion in selection of fuzzy expanded attributes is important for reduction fuzzy data sets. In this paper proposes a new criterion, to reduce fuzzy attributes and keep of some attributes which selected by using accuracy measure of fuzzy expanded attributes with respect to fuzzy decision attributes.