By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Y. Y. Yang ; Dept. of Automatic Control and Systems Eng., IMMPETUS, The University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom ; M. Mahfouf ; G. Panoutsos ; Q. Zhang
more authors

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.

Published in:

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Date of Conference:

27-30 June 2011