Abstract
Abstract One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser (GWO) algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, we report for the first time on a GWO algorithm along with the PNN for solving the classification of time series problem. PNN was used for obtaining the primary solution, and thereby the PNN weights were adjusted using the GWO for solving the time series data and further decreasing the error rate. In this study, the main goal was to investigate the application of the GWO algorithm along with the PNN classifier for improving the classification precision and enhancing the balance between exploitation and exploration in the GWO search algorithm. The hybrid GWO-PNN algorithm was used in this study, and the results obtained were compared with the published literature. The experimental results for six benchmark time series datasets showed that this hybrid GWO-PNN outperformed the PNN algorithm for the studied datasets. It has been seen that hybrid classification techniques are more precise and reliable for solving classification problems. A comparison with other algorithms in the published literature showed that the hybrid GWO-PNN could decrease the error rate and could also generate a better result for five of the datasets studied.
Highlights
One of the most important data mining tasks involves classification
To the best of our knowledge, we report for the first time on a grey wolf optimiser (GWO) algorithm along with the probabilistic neural network (PNN) for solving the classification of time series problem
The hybrid GWO-PNN algorithm was used in this study, and the results obtained were compared with the published literature
Summary
The input dataset is called the training set, which is used for building the model of a class label. This model is used for generating the output for cases when the preferred result is unknown. The NN models differ from one other based on their architecture, behaviour, and learning approaches Because of these differences, some of the models are more reliable than others and were used for solving many different problems like the time series classification problem. Some of the models are more reliable than others and were used for solving many different problems like the time series classification problem
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