An automatic Model for Managing Uncertainty and Rule Extraction in Form of Fuzzy Rules using Genetic Algorithm

Document Type : Original Article

Authors

1 Department of Computer Engineering, Toos Institute of Higher Education, Mashhad, Iran

2 PhD Student, Faculty of Engineering, Department of Computer Engineering, Islamic Azad University, North Tehran Branch, Tehran, Iran

3 Faculty of Engineering, Department of Computer Engineering, Islamic Azad University, Ghods Branch, Tehran, Iran

Abstract

In the last decade, the application of data mining techniques and intelligent methods for extracting knowledge automatically from the massive datasets has received a lot of attention. Considering the rule-based knowledge representation structure and its high capability for interpreting hidden patterns in information, the extraction of 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 presents the method of extracting rules directly from the data set and then examines the technique of extracting rules by the fuzzy classification method using the set of rules that was obtained in the previous step. At this stage, inconsistent, repetitive, and contradictory rules are 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 for them, we use the genetic algorithm to improve fuzzy rules in order to obtain the optimal set of rules. The proposed Fuzzy-Genetic method was applied to five well-known datasets where for three of them, results were more efficient than the SVM classical classification and Naïve Byes regression methods.

Keywords

References

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History

  • Receive Date: 04 May 2020
  • Revise Date: 23 December 2020
  • Accept Date: 13 January 2021

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