Generating Weighted Fuzzy Rules for Estimating Null Values Using an Evolutionary Algorithm

In this paper we have been trying 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 use 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 evolutionary algorithms. The parameters (operators) of the evolutionary algorithms are adapted via fuzzy systems. We have fuzzified the attribute values using membership functions shape, type and parameter values. The results of the evolutionary algorithms are the weights of the attributes. The different weight of attribute generates 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