Browsing by Author "Mazen, S"

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    A NOVEL OVERSAMPLING TECHNIQUE TO HANDLE IMBALANCED DATASETS
    (European Council for Modelling and Simulation, 06/01/2020) Mahmoud, A; Ali, F; El-Kilany, A; Mazen, S
    With the amount of data is growing extensively in different domains in the recent years, the data imbalance problem arises frequently. A dataset is called imbalanced when the data of a certain class has significantly more instances than that of other classes of the same dataset. This imbalanced nature of the data negatively affects the performance of a classifier since misclassification of data may cause data analysis results to be inaccurate and hence leads to wrong business decisions. This paper presents a study of the different techniques that are used to handle the imbalanced dataset, and finally proposes a novel oversampling technique to tackle the binary classification of imbalanced dataset problem. © ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther (Editors).