Masoud Muhammed Hassan1, Adel Sabry Eesa1,*, Ahmed Jameel Mohammed2, Wahab Kh. Arabo1
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280
- 04 June 2021
Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this More >
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