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Open AccessArticle

Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland
Author to whom correspondence should be addressed.
Entropy 2018, 20(4), 249;
Received: 25 January 2018 / Revised: 23 March 2018 / Accepted: 30 March 2018 / Published: 3 April 2018
Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q -generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors. View Full-Text
Keywords: artificial neural network; simulation study; generalized entropy artificial neural network; simulation study; generalized entropy
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Gajowniczek, K.; Orłowski, A.; Ząbkowski, T. Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks. Entropy 2018, 20, 249.

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