Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms

In recent years, some methods have been proposed to estimate null values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating null values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.