TL;DR
A hierarchical decision tree architecture for dispersed sources that removes the need to extract a validation subset from the test data during global training and achieves performance comparable to other hierarchical and ensemble approaches designed for distributed data.
Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors, significantly reduces the split search space for classification tasks while preserving predictive performance.
An Adaptive Ensemble Learning (AEL) framework that addresses this small-n-large-p regime through three coupled mechanisms, and was evaluated on six benchmark high-dimensional datasets containing between 617 and 12,600 features.
Tree-based models exhibited the largest reductions in accuracy and the steepest increases in calibration error under scarcity, whereas logistic regression preserved threshold balance and probability calibration at negligible computational cost.
CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification, generates diverse experts through threshold-based structured sampling while preserving the full label space, then estimates a class-wise trust score for each expert using a s...
The proposed approach combines interpretability, adaptive inductive bias selection, and probabilistic coherence within a unified mixture-of-experts framework to achieve interpretable expert assignments while achieving predictive performance competitive with homogeneous MoDT and Random Forests.
This paper introduces Strategic 16K, a carefully constructed, leakage-controlled corpus of 16,000 diplomatic cables sourced from the WikiLeaks Public Library of US Diplomacy (PlusD), and presents a systematic benchmark evaluating six model architectures spanning classical machine learning and transformer-based approach...
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