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pairwise threshold optimization

Pairwise threshold optimization is a technique in multiclass machine learning where individual decision thresholds are tuned separately for each binary subproblem formed by pairing two distinct classes, rather than optimizing a single joint threshold or boundary across all classes simultaneously. By breaking down a multiclass classification task into multiple pairwise comparisons, each binary classifier can be calibrated independently to accommodate local data distributions, adjust for class imbalance, and minimize pairwise misclassification error. The resulting optimized pairwise decisions or probability estimates are subsequently integrated—typically through pairwise coupling or voting mechanisms—to produce an accurate multiclass prediction while avoiding the computational complexity of high-dimensional joint optimization.

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