An automatic Model for Managing uncertainty in Fuzzy rule based knowledge extraction using Genetic algorithm

In the last decade, applications of data mining techniques and intelligent methods to extract knowledge automatically from the massive datasets has received a lot of attention. The Rule-based knowledge representation and their high capability to interpret this method in expressing hidden patterns in information, extracting hidden patterns in the form of a set of rules plays an important role in intelligent decision-making systems. After the pre-processing step, this article first goes to the method of extracting rules directly from the data set and then examines the technique of extracting rules by fuzzy classification method from the set of rules that was obtained in the previous step. At this stage, inconsistent, repetitive, and contradictory rules will be removed. Since one of the challenges in intelligent systems with the capability of managing uncertainty issues such as fuzzy systems, is that training does not take place in them, in order to achieve the optimal set of rules, go to the genetic algorithm and in addition to improve fuzzy rules. The proposed Fuzzy-Genetic method was evaluated on 5 well-known datasets, which in 3 datasets were more efficient than the classical classification methods of SVM and Naïve Byes regression.