Abstract
Many industrial tasks are related to the problem of the classification of unbalanced datasets. In these cases rare patterns of interest for the particular applications have to be detected among a much larger amount of patterns. Since data unbalance strongly affects the performance of standard classifiers, several ad–hoc methods have been developed. In this work the main techniques for handling class unbalance are depicted and three methods developed by the authors and based on the use of neural networks are described and tested on industrial case studies.
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Vannucci, M., Colla, V. (2018). Advanced Neural Networks Systems for Unbalanced Industrial Datasets. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_18
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DOI: https://doi.org/10.1007/978-3-319-56904-8_18
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