A. Zughrat

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Support Vector Machines (SVMs) is a popular machine learning technique, which has proven to be very effective in solving many classical problems with balanced data sets in various application areas. However, this technique is also said to perform poorly when it is applied to the problem of learning from heavily imbalanced data sets where the majority(More)
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 undersampling) class imbalance learning methods were applied to the data.(More)
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