Performances of Estimating Null Values using Noble Evolutionary Algorithm (NEAs) by Generating Weighted Fuzzy Rules

This paper Present a noble technique to estimate null values from relational database systems. At present some methods exist to estimate null values from relational database systems. The estimated accuracy of the existing methods are not good enough. We have used an advance technique for estimating null values in relational database systems. In our paper we present the technique to generate weighted Fuzzy rules from relational database systems for estimating null values using Noble Evolutionary algorithms. The parameters (operators) of the Evolutionary algorithms are adapted via Fuzzy systems. We have fuzzified the attribute values using membership functions shape. The results of the evolutionary algorithms are the weights of the attributes. The different weights of attribute generate a set of Fuzzy rules. From this we have obtained a set of rules. Our proposed techniques have a higher average estimated accuracy rate and able to estimate the null values in relational database systems.