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Ensemble Techniques for Handling Class Imbalance: Combining Strengths for Improved Performance

Ensemble techniques have been proposed as a promising solution to the class imbalance problem. Ensemble learning is a machine learning paradigm that combines multiple base classifiers to improve the overall performance. Ensemble methods can be roughly categorized into bagging-style methods, boosting-based methods, and hybrid ensemble methods. Bagging-style methods aim to reduce the variance of the base classifiers by generating multiple bootstrap samples from the original dataset. Boosting-based methods focus on improving the accuracy of the base classifiers by iteratively reweighting the training examples. Hybrid ensemble methods combine both bagging and boosting techniques to achieve better performance.

Class Imbalance Problem
The class imbalance problem is a common challenge in machine learning, particularly in medical data. It refers to the situation where the number of instances in one class is significantly lower than the other class(es). This leads to biased models that perform poorly in predicting the minority class, which is often the class of interest.

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