#feature selection

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9 papers match

stat.ML2026

Robust Wavelength Selection for Partial Least Squares Sugar Content Estimation Using Combinatorial Bayesian Optimization

Mitsunobu Kanebako, Ami S. Koshikawa, Masaru Hitomi +4

The paper proposes a combinatorial Bayesian optimization method for selecting wavelength regions in near‑infrared spectroscopy to improve sugar content prediction with partial leas…

#wavelength selection#near-infrared spectroscopy#partial least squares regression#bayesian optimization
stat.ML2026

On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems

Illia Horenko

The paper introduces Entropy-Optimal Manifold Regression (EOMR), a method that simultaneously selects relevant feature subsets and subspaces for nonlinear, nonstationary regression…

#feature selection#manifold learning#regression#chaotic dynamics
cs.LG2026

High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption

Loong Kuan Lee, Ragavi Krishnamoorthy, Nico Piatkowski

The paper introduces a k-order relaxation of the faithfulness assumption to handle parity-type dependencies and presents the k-order Markov blanket (kOMB) algorithm for discovering…

#markov blanket#faithfulness assumption#causal discovery#feature selection
eess.SP2026

A Data-Driven Vibration Analysis Framework for Micro-Motor Fault Diagnosis and Quality Control

Xuan Chen, Xinjun Zuo, Yancheng Bi +2

The paper presents a vibration‑based method that uses a custom accelerometer setup, feature extraction, random‑forest feature selection, and an SVM classifier to detect faults in m…

#vibration analysis#fault diagnosis#micro-motors#electric toothbrushes
stat.ML2026

ROOFS: RObust biOmarker Feature Selection

Anastasiia Bakhmach, Paul Dufossé, Simon Charpigny +4

ROOFS is a Python package that benchmarks many biomarker feature‑selection methods on biomedical data, providing stability, robustness, and predictive performance metrics to help c…

#feature selection#biomarker discovery#clinical predictive modeling#robustness
cs.LG2026

Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study

Daniel Grillmeyer, Marius Hadry, Michael Stenger +3

The paper introduces a clustering‑based wrapper method (CSFS) for automatically selecting input variables in wind and solar power forecasting, showing comparable accuracy to standa…

#renewable energy prediction#feature selection#wind power#solar power
eess.IV2026

A Hybrid Framework for Blood Vessel Morphology Classification: Discrete Geometry-based Tortuosity Feature Measurement, Information Gain-based Feature Selection, and Random Forest Classification

Yu Zhong, Jingzhi Guo, Luyao Li +5

The paper presents a framework that quantifies and classifies internal carotid artery tortuosity using discrete geometric features, information‑gain based feature selection, and a…

#vascular tortuosity#feature selection#random forest#morphological classification
cs.LG2026

From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models

Muhammad Ashad Kabir, Sirajam Munira

The paper investigates using large language models in a zero-shot setting to screen for chronic kidney disease by converting a compact set of community-accessible clinical features…

#chronic kidney disease screening#zero-shot learning#large language models#feature selection
cs.CR2026

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

Abu Fuad Ahmad, Istiaque Ahmed

The paper introduces BARS, a benign‑anchored filter for feature selection that reduces false positive rates in network intrusion detection systems while keeping low computational o…

#intrusion detection#feature selection#false positive reduction#network security

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