Attribute reduction with imputation of missing data using fuzzy-rougsh set

Abstract

Attribute Reduction and missing data imputation have considerable influence in classification or other data mining task. New hybridization methodology like fuzzy rough set is more robust method to deal with imprecision and uncertainty for discrete as well as continuous data. Fuzzy rough attribute reduction with imputation (FRARI) algorithm has been proposed for attribute reduction with missing value imputation. So using FRARI algorithm complete reduce data set can be generated which has a great importance in different branches of artificial intelligence for data mining from databases. Efficiency and effectiveness of the proposed algorithm has been shown by experiment with real life data set.