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Deep learning class imbalance problem:
Deep learning has revolutionized the way we design solution to complex problem. Just like humans learn by experience, machine learning algirithms has enabled to copy the learning mecanism to “train” a network capable of learning complex tasks. These algorithems rellay on labled data with abudent and huge variance. The performance of learning is directly proportional to the quality of the training dataset. If the dataset is dominated by a class the learner will be biased towords that majority class and may ignore the minority class. Since high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. One should be carefful when to dealing with this problem.
Class imbalance problem :
class imbalance occurs when one class, the minority group, contains significantly fewer samples than the other class, the majority group.
How to deal with class imbalance problem:
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