A Neuro-Fuzzy Classifier Based on Evolutionary Algorithms

Neuro-fuzzy systems have been proved effective in training classifiers, especially when it comes to noisy, inaccurate or incomplete datasets. For this reason, and due to their simple comprehensible nature, these systems have become popular in designing classifiers. One of the major challenges in designing a neuro-fuzzy classifier is achieving the optimum system parameters such as the type and position of the membership function as well as its training method. These factors could affect the function of the classifier significantly. In this paper, a novel method based on evolutionary algorithms such as inclined planes optimization algorithm (IPO), particle swarm optimizer (PSO) and genetic algorithm (GA) is introduced to design a neuro-fuzzy classifier in such a way that the accuracy is increased and the error rate is minimized. To prove the efficiency of the proposed method, several experiments are conducted on well-known datasets with different number of classes and different feature vector lengths. Results indicate that the proposed evolutionary-based neuro-fuzzy classifier is superior to a normal neuro-fuzzy classifier in terms of accuracy. In addition, experiments showed that the proposed method is able to properly classify the data with a relatively high stability.