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Adaptive neural-fuzzy inference system for classification of rail quality data with bootstrapping-based over-sampling

Yang, Y. Y. and Mahfouf, M. and Panoutsos, G. and Zhang, Qian and Thornton, S. (2011) Adaptive neural-fuzzy inference system for classification of rail quality data with bootstrapping-based over-sampling. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011). IEEE, pp. 2205-2212. ISBN 978-1-4244-7315-1. E-ISBN 978-1-4244-7316-8. (doi:10.1109/FUZZY.2011.6007729) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://doi.org/10.1109/FUZZY.2011.6007729

Abstract

An iterative bootstrapping-based data over-sampling strategy is presented in this paper together with an adaptive neural-fuzzy inference system (ANFIS) to deal with a severely imbalanced data modelling problem. As real industrial data are often very large, containing hundreds of process variables and a huge number of data records, the selection of a compact set of input variables becomes critical for any successful modelling and analysis operations. Significant efforts have been devoted to identifying the most relevant input variables through correlation analysis and neural network based forward input selection. An optimal majority to minority class data ratio, which controls the level of data imbalance for model training, is then determined through the iterative bootstrapping process such that the combined sensitivity and specificity performance is optimised. The iterative bootstrapping ANFIS modelling strategy is then applied to a real industrial case study for rail quality classification, with the original data being provided by Tata Steel Europe. Preliminary results show a good overall performance through the iterative bootstrapping data over-sampling ANFIS modelling.

Item Type: Book section
DOI/Identification number: 10.1109/FUZZY.2011.6007729
Uncontrolled keywords: data models; rails; correlation; artificial neural networks; steel; computational modeling
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:24 UTC
Last Modified: 20 Sep 2019 10:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50540 (The current URI for this page, for reference purposes)
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