Fuzzy Rough Set Conditional Entropy Attribute Reduction Algorithm

Abstract

Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data processing, big data processing that is mixed with great missing date must be processed rapidly. The Rough Set was generated to deal with the large data. The QuickReduct is a popular attribute algorithm as the attribute reduction of big database. But less effort has been put on fuzziness and vagueness data. Considering this requirement this paper proposes an improved attribute reduction based on condition entropy of fuzzy rough sets (FRCE) which can deal with the continuous and fuzzy data. This algorithm rewrites the expression of condition entropy by using the information theory. Last this paper takes the UCI database to simulate the efficiency of this algorithm.

DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4795