Abstract
Inversion of a neural network trained on some classification problem has been an important issue related to the explanation of the neural classification function. Inversion based on Interval Analysis (IA) [1] showed that a reliable estimation of the neural network domain of validity is feasible and a number of quantitative issues arise from this inversion. This paper deals with the investigation of these quantitative issues and more precisely with those concerning the evaluation of the neural network classification function in terms of generalization, comparison of different network models and classification accuracy. Preliminary experimental results indicate that the IA-based inversion can offer a solid basis towards reliable evaluation of the neural classification function.
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The authors would like to thank the anonymous reviewers for their comments and suggestions on earlier draft of the manuscript.
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Adam, S.P., Likas, A.C., Vrahatis, M.N. (2017). Interval Analysis Based Neural Network Inversion: A Means for Evaluating Generalization. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_27
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