Improved water cycle algorithm with probabilistic neural network to solve classification problems

Abstract

Classification is achieved through the categorisation of objects into predefined categories or classes, where the categories or classes are created based on a similar set of attributes of the object. This is referred to as supervised learning. Numerous methodologies have been formulated by researchers in order to solve classification problems effectively. These methodologies exhibit an uncomplicated structure and fast training, and are based on artificial intelligence, such as the probabilistic neural network (PNN). In this study, techniques to improve the accurateness of the PNN in solving classification problems have been analysed with the help of the water cycle algorithm (WCA), which is a population-based metaheuristic that imitates the water cycle in the real world. In the recommended solution, near-optimal solutions are created in order to regulate the arbitrary parameter selection of the PNN. In this study, it has also been suggested that the enhanced WCA (E-WCA) can be used to attain a balance between exploitation and exploration, so that premature conjunction and immobility of the population can be avoided. With the help of 11 standard benchmark datasets, the recommended solutions were verified. The results of the experiment substantiated that the WCA and E-WCA are capable of improving the weight parameters of the PNN, thereby imparting improved performance with respect to convergence speed and classification accuracy, compared with the initial PNN classifier.