ensemble-learning

This paper introduces a framework using AUC bounds as a differentiable objective for anomaly-free self-optimization of anomaly detection systems, achieving performance gains over conventional model selection methods.

A comparative study of temporal deep learning architectures for physiological emotion recognition using multimodal wearable datasets, evaluating LSTM, TCN, and Transformer models under different sensing configurations.

This paper introduces a corpus of 10,000 annotated Bangla sentences for function classification and benchmarks various models, achieving 0.95 accuracy with a double-level ensemble using TF-IDF features.

This paper proposes a hybrid semantic context-enhanced ensemble learning approach for wind power ramp-event forecasting, demonstrating small but statistically significant improvements over baseline models with uncertainty-aware evaluation.

This paper proposes a comparative system for classifying Parkinson's disease severity using triaxial IMU sensors and ensemble learning, with LightGBM achieving the best performance at around 97% accuracy across metrics.

This paper introduces CG4AI, a framework that uses column generation to train AI models while enforcing hard linear constraints on their outputs, demonstrating applications in digit classification and network routing with improved feasibility and accuracy.

A new paper claims that an ensemble of Qwen3.8-27B models achieves coding performance comparable to Fable-5 on LiveCodeBench, potentially at a significantly lower cost.

The paper proposes a behavior-aware framework for constructing diverse LLM crowds to improve future prediction, demonstrating that smaller, behaviorally diverse groups can outperform larger ones while reducing inference costs.

The paper proposes a framework for interpretable multimodal classification using Linear Discriminant Tree Ensembles, which balance accuracy and interpretability, outperforming Transformer models in F1-mod gains and human-annotator agreement scores.

This paper revisits energy-based models for tabular anomaly detection, demonstrating that combining Deep Boltzmann Machine energy scores with autoencoder reconstruction scores significantly improves performance on benchmark datasets.

REATS is a new approach that uses LLM reasoning to perform interpretable, sample-adaptive ensemble learning for time series forecasting. It combines textual and numerical features with chain-of-thought reasoning and a two-stage fine-tuning framework, outperforming competitive baselines on eight benchmarks.

The paper proposes intuitionistic fuzzy deep RVFL (IF-dRVFL) and ensemble deep RVFL (IF-edRVFL) frameworks that use sample neighborhood information to improve robustness against noise and outliers in classification tasks, outperforming existing SOTA fuzzy and non-fuzzy approaches on benchmark datasets.

This paper proposes a hierarchical, interpretable-by-design pivot selection model based on proximity trees and ensemble learning. It is data-modality-agnostic and demonstrates competitive results across tabular, text, image, and time-series datasets.

This paper proposes LFS-FRAME, a leakage-free stacked ensemble framework integrating Kolmogorov-Arnold Networks and XGBoost for robust multiclass classification, achieving 89.85% accuracy on major families and 81.74% on sub-families.

A critical reflection on whether using only three base models for millions of personal AI agents can produce genuinely diverse deliberation, arguing that correlated errors across models may create false unanimity and seeking operational metrics—drawn from ensemble learning—to measure true human representational diversity.

TabLoRA proposes a parameter-efficient neural ensemble method for large-scale tabular data by sharing a common backbone with predictor-specific low-rank adaptations, achieving competitive performance against GBDTs and deep learning baselines.

A critique of a popular quant thread selling a 77% win-rate random forest strategy, noting that the method is standard ensemble learning from a free Stanford lecture and that past performance does not guarantee future results.

This paper proposes SCBoost, a boosting framework that reduces learner redundancy by projecting residuals onto the orthogonal complement of previous predictions and using covariance-regularized weighting, with theoretical guarantees and strong empirical performance.

Introduces Simplex-Constrained Sparse Bagging (SCSB), a post-training framework that optimizes estimator weights over the probability simplex using out-of-bag samples, achieving up to 96% ensemble compression and improved calibration.

This paper presents WISE-HAR, an ensemble deep learning framework for WiFi-based human activity recognition, achieving robust performance and generalization across scenarios with minimal accuracy drops.