A Novel Fuzzy Genetic Annealing Classification Approach
Maziyar Baran Pouyan, Hamid Mohamadi, Mohammad Saniee Abadeh, Ali Foroughifar · 2009
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at the core of simulated annealing heuristic. Results of proposed approach have been compared with several well-known methods such as Naive Bayes, Support Vector Machine, Decision Tree, k-NN, and GBML, and show that our method performs the classification task as well as other famous algorithms.