Design of fuzzy rule based classifier using the monkey algorithm [1]

T2 - IS - KW - fuzzy classifier KW - optimization of fuzzy parameters KW - monkey algorithm KW - fuzzy rule extraction AB - Ilya A. Hodashinsky - Professor, Department of Complex Information Security of Computer Systems, Tomsk State University of Control Systems and Radioelectronics (TUSUR) Address: 40, Prospect Lenina, Tomsk, 634050, Russian FederationE-mail: hodashn@rambler.ruSergey S. Samsonov - Student, Department of Complex Information Security of Computer Systems, Tomsk State University of Control Systems and Radioelectronics (TUSUR) Address: 40, Prospect Lenina, Tomsk, 634050, Russian FederationE-mail: samsonicx@mail.ru This article presents an approach for building fuzzy rule based classifiers. A fuzzy rule-based classifier consists of IF-THEN rules with fuzzy antecedents (IF-part) and the class marks in consequents (THEN-part). Antecedent parts of the rules break down the input feature space into a set of fuzzy areas, and consequents define the classifier exit, marking these areas with a class mark. Two main phases of building the classifier are selected: generating the fuzzy rule base and optimizing the rule antecedent parameters. The classifier structure was formed by an algorithm for generating the rule base by extreme features found in the training sample. The peculiarity of this algorithm is that it is generated according to one classification rule for each class. The rule base formed by this algorithm has as low as practicable size in classification of a given data set. The optimization of parameters of antecedents of the fuzzy rules is implemented using the monkey algorithm adapted for these purposes, which is based on observations of monkey migration in the highlands. In the process of the algorithm work, three operations are performed: climb process, watch jump process and somersault process. One of the algorithm’s advantages in solution of high-dimension optimization problems is calculation of the pseudo-gradient of the objective function. Irrespective of the dimension at each iteration of the algorithm execution only two values of the objective function are to be calculated. The effectiveness of fuzzy rule-based classifiers built with the use of the proposed algorithms was checked on actual data from the KEEL-dataset repository. A comparative analysis was conducted using the known analog algorithms "D-MOFARC" and "FARC-HD". The number of rules used by the classifiers built with the use of the algorithms so developed is much lower than the number of rules in analog classifiers with a comparable classification accuracy, that points to the highest interpretability of the classifiers built with the use of the proposed approach.[1] This work was supported by the Ministry of Education and Science of the Russian Federation, agreement no. 8.9628.2017/BP AU - Ilya Hodashinsky AU - Sergey Samsonov UR - https://bijournal.hse.ru/en/2017--1 (39)/206138305.html PY - 2017 SP - 61-67 VL -