Solving imbalanced data problems by applying conformal prediction

Conformal prediction is a technique that can be applied to handle imbalanced data problems. Here are a few ways it can be used:

• Graceful handling of imbalanced datasets: conformal prediction can gracefully handle large imbalanced datasets. It strictly defines the level of similarity needed, removing any ambiguity. It can handle severely imbalanced datasets with ratios of 1:100 to 1:1000 without oversampling or undersampling.
• Local clustering conformal prediction (LCCP): LCCP incorporates a dual-layer partitioning approach within the conformal prediction framework. Initially, it segments the imbalanced training dataset into subsets based on class taxonomy. Then, it further divides the examples from the majority class into subsets using clustering techniques. The goal of LCCP is to offer reliable confidence levels for its predictions while also enhancing the efficiency of the prediction process.
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