Abstract
The design and implementation of Data-Driven Fuzzy Models (DDFMs) to learn balanced industrial/manufacturing data has demonstrated to be a popular machine learning methodology. However, DDFMs have also proven to perform poorly when it comes to learn from heavily imbalanced data, particularly in manufacturing systems. In order to tackle real-world imbalanced problems, we propose a DDFM for rail manufacturing classification. This framework includes Feature Selection, iterative information granulation, and a Fuzzy Decision Engine (FDE) that is based on an Interval Type-2 Radial Basis Function Neural Network (IT2-RBF-NN). The proposed modelling framework is then tested against a real manufacturing case study provided by TATA Steel, UK. Simulation results showed the proposed framework outperformed the generalisation properties of various well known methodologies including a DDFM that employs the RBF-NN of type-1.
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- 1.
getFE and getNE denote the operation for getting the First and Next Element of the list.
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Acknowledgements
The authors would like to acknowledge Innovate UK for the financial support, under grant agreement 101947, SPEEAK-PC and TATA STEEL, UK for providing the manufacturing case study and associated data.
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Rubio-Solis, A., Baraka, A., Panoutsos, G., Thornton, S. (2018). Data-Driven Interval Type-2 Fuzzy Modelling for the Classification of Imbalanced Data. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Practical Issues of Intelligent Innovations. Studies in Systems, Decision and Control, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-78437-3_3
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DOI: https://doi.org/10.1007/978-3-319-78437-3_3
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