Abstract
Abstract This study aims at designing a modelling architecture to deal with the imbalanced data relating to the production of rails. The modelling techniques are based on Support Vector Machines (SVMs) which are sensitive to class imbalance. An internal (Biased Fuzzy SVM) and external (data under-sampling) class imbalance learning methods were applied to the data. The performance of the techniques when implemented on the latter was better, while in both cases the inclusion of a fuzzy membership improved the performance of the SVM. Fuzzy C-Means (FCM) Clustering was analysed for reducing the number of support vectors of the Fuzzy SVM model, concluding it is effective in reducing model complexity without any significant performance deterioration.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
