Improved water cycle algorithm with probabilistic neural network to solve classification problems
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Improved water cycle algorithm with probabilistic neural network to solve classification problems Mohammed Alweshah1 Sara Tedmori2
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Maria Al-Sendah1 • Osama M. Dorgham1 • Ammar Al-Momani1
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Received: 9 March 2018 / Revised: 9 March 2018 / Accepted: 25 December 2019 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
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 (EWCA) 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. Keywords Water cycle algorithm Probabilistic neural networks Classification problem Metaheuristics
1 Introduction Classification is a form of supervised machine learning that is commonly used to support the decision-making procedure in different areas such as science, medicine, business
& Mohammed Alweshah [email protected] Maria Al-Sendah [email protected] Osama M. Dorgham [email protected] Ammar Al-Momani [email protected] Sara Tedmori [email protected] 1
Al-Balqa Applied University, Al-Salt, Jordan
2
Princess Sumaya University for Technology, Amman, Jordan
and industry. Classification challenges appear when an object has to be classified based on a number of object attributes [1]. The most crucial step in the process of classification is to select the right classification technique that can relate to all real-world problems. The classification techniques are open for use only after the completion of primary testing and after the results have been deemed as acceptable [2]. Several techniques have been formulated by researchers based on artificial intelligence, such as artificial neural networks (ANNs) [3], naive Bayes classifier [4]
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