stratified-quota-sampling

To balance a long-tailed dataset without losing rare classes, stratified quota sampling normalizes per-class counts with Box-Cox, bins classes into LOW/MID/HIGH tiers at ±1σ, and allocates Fibonacci-family quotas to scale selection sublinearly while preserving rare-class presence.

The best way to sample a fixed-budget subset from an imbalanced corpus is to apply relevance-weighted sampling within Box-Cox normalized tiers, ensuring higher-quality items are preferred while maintaining coverage across skewed class frequency distributions.

Yes, reproducible results are possible when sampling from class-imbalanced data by setting a deterministic seed, which ensures the same subset is drawn from the Box-Cox normalized and Fibonacci-quota-allocated tiers every time.

Box-Cox normalization helps with class imbalance by reshaping raw per-class counts toward near-normality, which allows accurate z-score derivation for assigning classes into LOW, MID, and HIGH strata before quota allocation.

Yes, this stratified sampling approach works for sparse entity coverage in hierarchical corpora by applying per-item relevance weights within each class tier, ensuring rare categories remain visible under a fixed selection budget.

Naive sampling methods starve rare classes in long-tailed datasets because they either flood frequent classes or starve rare classes, leading to distorted training and weak coverage, whereas Fibonacci-style quota allocation scales selection sublinearly to prevent this.