被撤回的出版物: Coronavirus herd immunity optimizer to solve classification problems (Retracted article. See MAY, 2023)

Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and beta-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature.

Automated summary

This paper introduces the application of classification technique, coronavirus herd immunity optimizer algorithm, probabilistic neural network, and their effectiveness in solving classification problems. The experimental results show that the proposed CHIO-PNN method achieves high classification accuracy on multiple datasets, and it converges faster.