Swarm intelligence and neural network for data classification

Abstract

Classification process is one of the most important operations implemented on the huge data warehouses in order to classify the data. Availability of huge amounts of data increased the need for effective techniques to analyze and classify data accurately. Many algorithms in the field of swarm intelligence are able to contribute to improve the classification accuracy using the optimal algorithm methods. The optimal algorithms are used to select optimal features set. From this perspective, these algorithms are used to select the optimal feature of weights and biases for artificial neural network. The proposed method in this article is based on the two algorithms in the field of swarm intelligence, which are used as the new training method for artificial neural network in order to overcome the deficiency in the traditional training algorithms and get a high classification accuracy. The hybrid ABC and PSO is used as new training method for feed-forward neural network, the proposed method is tested in terms of classification accuracy on several datasets. The result show that the performance of classification accuracy of the method is better than the other classification algorithms.