… various multi-class imbalanced datasets. Therefore, in this study, we examine the performance
of 14 most significant boosting algorithms on 19 multi-class imbalanced conventional and …

… a new taxonomy for class imbalanced learning techniques. … methods to deal with similar
problems in regression tasks. A new taxonomy for class imbalanced learning techniques is …

… 1) Contrary to [10] and [12] we do not limit ourselves to a formal definition of the class
imbalanced classification problem. Instead, we take a more pedagogical route by discussing (often …

… literature review. In this study, the importance of organizing imbalanced data is explained
and the techniques suggested by the different scholars to counterbalance the skewed nature of …

… review and related work on class imbalance problems and applications of ensemble learning
and data … Section 3 presents a review of ensemble learning and data augmentation for CI …

… study is to examine existing deep learning techniques for addressing class imbalanced data…
and filtered, removing those that did not demonstrate learning from class imbalanced data …

… problem into a twoclass imbalanced subtask that can be learned by binary classifier techniques.
In the … Clustering and combined sampling approaches for multi-class imbalanced data …

… imbalance problems A Novel ensemble method for classifying imbalanced data. [70] No
loss of information and remove chances of mistakes For only binary class imbalanced data. …

… consisting of application, approach and use cases for each of the domain discussed above.
Hence, this section discusses several applications where class imbalanced data/ problem …

… This review aims to examine the state of the art of semi-supervised learning (SSL)
techniques for addressing class imbalanced data. Class imbalance is inherent in many real-world …