arXiv:2406.10087v2 Announce Type: replace-cross 
Abstract: Certain cancer types, notably pancreatic cancer, are difficult to detect at an early stage, motivating robust biomarker-based screening. Liquid biopsies enable non-invasive monitoring of circulating biomarkers, but typical machine learning pipelines for high-dimensional tabular data (e.g., random forests, SVMs) rely on expensive hyperparameter tuning and can be brittle under class imbalance. We leverage a meta-trained Hyperfast model for classifying cancer, accomplishing the highest AUC of 0.9929 and simultaneously achieving robustness especially on highly imbalanced datasets compared to other ML algorithms in several binary classification tasks (e.g. breast invasive carcinoma; BRCA vs. non-BRCA). We also propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks, achieving an incremental increase in accuracy (0.9464) while merely using 500 PCA features; distinguishable from previous studies where they used more than 2,000 features for similar results. Crucially, we demonstrate robustness under class imbalance: empirically via balanced accuracy and minority-class recall across cancer-vs.-noncancer and cancer-vs.-rest settings, and theoretically by showing (i) a prototype-form final layer for Hyperfast that yields prior-insensitive decisions under bounded bias, and (ii) minority-error reductions for majority vote under mild error diversity. Together, these results indicate that pre-trained tabular models and simple ensembling can deliver state-of-the-art accuracy and improved minority-class performance with far fewer features and no additional tuning.

تقدم دراسة جديدة نموذج Hyperfast المدرب مسبقًا لتصنيف السرطان، محققًا AUC مرتفعًا قدره 0.9929، وهو فعال بشكل خاص لمجموعات البيانات غير المتوازنة. يتفوق هذا النموذج على طرق التعلم الآلي التقليدية مثل الغابات العشوائية وSVM، التي غالبًا ما تتطلب ضبطًا مكلفًا للمعلمات. بالإضافة إلى ذلك، تم اقتراح نموذج مجموعة جديد يجمع بين نموذج Hyperfast المدرب مسبقًا وXGBoost وLightGBM، مما يحسن الدقة لمهام التصنيف متعددة الفئات باستخدام 500 ميزة PCA فقط.

Un nuevo estudio presenta un modelo Hyperfast meta-entrenado para la clasificación del cáncer, logrando un alto AUC de 0.9929, especialmente efectivo para conjuntos de datos desbalanceados. Este modelo supera a los métodos tradicionales de aprendizaje automático como los bosques aleatorios y SVM, que a menudo requieren una extensa sintonización de hiperparámetros. Además, se ha propuesto un nuevo modelo de conjunto que combina Hyperfast, XGBoost y LightGBM, mejorando la precisión para tareas de clasificación multiclase utilizando solo 500 características PCA.

Une nouvelle étude présente un modèle Hyperfast méta-entraîné pour la classification du cancer, atteignant un AUC élevé de 0,9929, particulièrement efficace pour les ensembles de données déséquilibrés. Ce modèle surpasse les méthodes d'apprentissage automatique traditionnelles telles que les forêts aléatoires et les SVM, qui nécessitent souvent un réglage hyperparamétrique étendu. De plus, un nouveau modèle d'ensemble combinant Hyperfast, XGBoost et LightGBM a été proposé, améliorant la précision pour les tâches de classification multi-classes en utilisant seulement 500 caractéristiques PCA.

A new study presents a meta-trained Hyperfast model for cancer classification, achieving a high AUC of 0.9929, particularly effective for imbalanced datasets. This model outperforms traditional machine learning methods like random forests and SVMs, which often require extensive hyperparameter tuning. Additionally, a novel ensemble model combining Hyperfast, XGBoost, and LightGBM has been proposed, enhancing accuracy for multi-class classification tasks using only 500 PCA features.

Provably Robust Pre-Trained Ensembles for Biomarker-Based Cancer Classification

arXiv:2511.17275v1 Announce Type: new 
Abstract: Premium automotive manufacturers face increasingly complex forecasting challenges due to high product variety, sparse variant-level data, and volatile market dynamics. This study addresses monthly automobile demand forecasting across a multi-product, multi-market, and multi-level hierarchy using data from a German premium manufacturer. The methodology combines point and probabilistic forecasts across strategic and operational planning levels, leveraging ensembles of LightGBM models with pooled training sets, quantile regression, and a mixed-integer linear programming reconciliation approach. Results highlight that spatiotemporal dependencies, as well as rounding bias, significantly affect forecast accuracy, underscoring the importance of integer forecasts for operational feasibility. Shapley analysis shows that short-term demand is reactive, shaped by life cycle maturity, autoregressive momentum, and operational signals, whereas medium-term demand reflects anticipatory drivers such as online engagement, planning targets, and competitive indicators, with online behavioral data considerably improving accuracy at disaggregated levels.

تم إجراء دراسة حول توقعات الطلب على السيارات، مع التركيز على مصنع سيارات فاخرة ألماني. تستخدم الدراسة مجموعة من تقنيات النمذجة الزمانية والمكانية والهرمية، مستفيدة من نماذج LightGBM لتعزيز دقة التوقعات في ظل ديناميكيات سوق معقدة وتنوع كبير في المنتجات.

Se ha realizado un estudio sobre la previsión de la demanda de automóviles, centrándose en un fabricante premium alemán. La investigación emplea una combinación de técnicas de modelado espaciotemporal y jerárquico, utilizando modelos LightGBM para mejorar la precisión de las previsiones en medio de dinámicas de mercado complejas y una gran variedad de productos.

Une étude a été réalisée sur la prévision de la demande automobile, en se concentrant sur un fabricant premium allemand. La recherche utilise une combinaison de techniques de modélisation spatiotemporelle et hiérarchique, en s'appuyant sur des modèles LightGBM pour améliorer la précision des prévisions face à des dynamiques de marché complexes et à une grande variété de produits.

A study has been conducted on automobile demand forecasting, focusing on a German premium manufacturer. The research employs a combination of spatiotemporal and hierarchical modeling techniques, utilizing LightGBM models to enhance forecast accuracy amidst complex market dynamics and product variety.

Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information

arXiv:2506.08274v5 Announce Type: replace-cross 
Abstract: This research addresses the critical lack of comprehensive studies on feature scaling by systematically evaluating 12 scaling techniques - including several less common transformations - across 14 different Machine Learning algorithms and 16 datasets for classification and regression tasks. We meticulously analyzed impacts on predictive performance (using metrics such as accuracy, MAE, MSE, and $R^2$) and computational costs (training time, inference time, and memory usage). Key findings reveal that while ensemble methods (such as Random Forest and gradient boosting models like XGBoost, CatBoost and LightGBM) demonstrate robust performance largely independent of scaling, other widely used models such as Logistic Regression, SVMs, TabNet, and MLPs show significant performance variations highly dependent on the chosen scaler. This extensive empirical analysis, with all source code, experimental results, and model parameters made publicly available to ensure complete transparency and reproducibility, offers model-specific crucial guidance to practitioners on the need for an optimal selection of feature scaling techniques.

دراسة حديثة نُشرت على arXiv قامت بتقييم منهجي لـ 12 تقنية لتقييس الميزات عبر 14 خوارزمية تعلم آلي و16 مجموعة بيانات، كاشفة عن تباينات كبيرة في الأداء في نماذج مثل الانحدار اللوجستي وSVM، بينما أظهرت طرق التجميع قوة مستقلة عن التقييس.

Un estudio reciente publicado en arXiv evaluó sistemáticamente 12 técnicas de escalado de características a través de 14 algoritmos de aprendizaje automático y 16 conjuntos de datos, revelando variaciones significativas en el rendimiento de modelos como la regresión logística y los SVM, mientras que los métodos de ensamblaje mostraron robustez independientemente del escalado.

Une étude récente publiée sur arXiv a évalué systématiquement 12 techniques de mise à l'échelle des caractéristiques à travers 14 algorithmes d'apprentissage automatique et 16 ensembles de données, révélant des variations de performance significatives dans des modèles tels que la régression logistique et les SVM, tandis que les méthodes d'ensemble ont montré une robustesse indépendamment de la mise à l'échelle.

A recent study published on arXiv systematically evaluated 12 feature scaling techniques across 14 machine learning algorithms and 16 datasets, revealing significant performance variations in models like Logistic Regression and SVMs, while ensemble methods showed robustness regardless of scaling.

Provably Robust Pre-Trained Ensembles for Biomarker-Based Cancer Classification

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