A Hybrid Material Generation Algorithm with Probabilistic Neural Networks for Solving Classification Problems

Classification is based on machine learning, in which each element in a set of data is classified into one of a predetermined set of groups. In data mining, an artificial neural network (ANN) is the most significant methodology because of the exact results obtained through this algorithm and applied in solving many classification problems. ANN consists of a group of types of feed-forward networks, feed-back network, RFB networks, and the probabilistic neural networks (PNN). For classification issues, the PNN is frequently utilized. The primary goals of this research are to fine-tune the weights of neural networks to enhance the classification accuracy. To accomplish this goal, the Material Generation Algorithm (MGA) was investigated with PNN in a hybrid model. Newly, the hybridization of algorithms is ubiquitous and it has led to the development of unique procedures that outperform those that use a single algorithm. Several distinct classification tasks are used to test the efficiency of the suggested (MGA-PNN) approach. The MGA algorithm's efficiency is evaluated using the PNN training outcomes generated, and its outcomes are compared to that of other optimization strategies. By 11 benchmark datasets, the suggested algorithm's performance in terms of classification accuracy is evaluated. The outcomes display that the MGA outperforms the biogeography based optimization, firefly method in terms of classification accuracy.

@article{Wedyan2022,
  title     = {A Hybrid Material Generation Algorithm with Probabilistic Neural Networks for Solving Classification Problems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Mohammad Wedyan and Omar Alshaweesh and Enas Ramadan and Ryan Alturki and Foziah Gazzawe and Mohammed J. Alghamdi},
  doi       = {10.14569/IJACSA.2022.0130532},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130532}
}