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Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks.
Krzysztof Gajowniczek1, Arkadiusz Orłowski1, Tomasz Ząbkowski1
1Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces Tsallis statistics as a novel error measure for artificial neural networks (ANNs). The Tsallis entropy error function demonstrates effectiveness in handling challenges like class imbalance and noisy data in ANNs.
Area of Science:
- Machine Learning
- Computational Statistics
Background:
- Artificial neural networks (ANNs) are widely used classifiers in diverse fields.
- Various error functions exist to enhance ANN predictive power.
- Tsallis statistics, though successful in other machine learning areas, is underexplored in ANNs.
Purpose of the Study:
- To investigate the q-generalized function based on Tsallis statistics as an alternative error measure for ANNs.
- To evaluate the performance and identify the strengths and weaknesses of this novel error function.
Main Methods:
- Extensive simulations were conducted using an artificial benchmarking dataset.
- The proposed Tsallis entropy error function was implemented and tested within the ANN framework.
Main Results:
- The Tsallis entropy error function shows successful integration into ANNs.
- Satisfactory results were achieved, demonstrating robustness against class imbalance and noisy data.
- The function also proved effective with non-informative predictors.
Conclusions:
- Tsallis entropy error function offers a viable alternative for ANNs.
- It provides a robust performance, particularly in challenging data scenarios.
- Further exploration of Tsallis statistics in machine learning is warranted.
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