Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
Authors: Youngjoon Park
Summary
arXiv:2605. 20716v1 Announce Type: cross Abstract: Random forests aggregate tree votes by simple majority, treating all trees as equally informative.
Relevance
Read next because Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, under, compare, without. Source: arxiv stat.ML (Machine Learning).
Threat model
Potential threat/caveat for clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)": this item discusses bias, confound, benchmark.
Abstract
arXiv:2605.20716v1 Announce Type: cross Abstract: Random forests aggregate tree votes by simple majority, treating all trees as equally informative. We observe that the topological pattern along each tree's root-to-leaf decision path -- where and how often the dominant class label flips along it -- carries a signal of tree reliability that is exploitable for per-sample reweighting. The naive use of this signal is structurally confounded with the predicted class, so we propose a class-conditional ratio weighting that guarantees zero expected class bias by construction. On 30 binary classification benchmarks under a shared-forest, shared-split protocol with 30 repeats, the proposed method is the only one among four compared schemes -- RF, weighted RF, KNORA-Eliminate, KNORA-Union -- to yield a statistically significant accuracy improvement over RF (Wilcoxon p = 0.018), while the three alternatives all fail to do so (p > 0.5). It is also the only scheme without majority-recall regressions, with minority-recall regressions limited to 3/30 datasets -- a one-sided loss to which classical dynamic ensemble selection methods are susceptible. The gain is robust across forest sizes from 100 to 1000 trees.