Multi-feature fusion RFE random forest for schizophrenia classification and treatment response prediction by Chang Wang, Rui Zhang, Jiyuan Zhang, Yaning Ren, Ting Pang, Xiangyu Chen, Xiao Li, Zongya Zhao, Yongfeng Yang, Wenjie Ren, Yi Yu

“…We developed a multi-feature fusion recursive feature elimination random forest (RFE-RF) approach for SZ classification and treatment response prediction. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. Secondly, a recursive sparse self-attention mechanism is designed to aggregate representative feature maps under linear complexity, adaptively selecting weight distribution and reducing information redundancy during computation. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…This model integrates a Random Forest (RF), three-layer Deep Neural Networks (DNN), Support Vector Machine Radial (SVM-R), and a Gradient Boosting Model (GBM) meta layer to enhance prediction accuracy. The Recursive Feature Elimination (RFE) algorithm is then employed to identify the most influential SSMs for conflict prediction in each scenario. …”
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Temporal generalization in evergreen leaf type classification using tailored Sentinel-2 composites by Peter Hofinger, Jan Dempewolf, Simon Ecke, Hans-Joachim Klemmt

“…Key contributions include recursive feature elimination to identify the most relevant spectral bands and indices, and optimizing compositing methods to boost classification accuracy, balancing robustness and temporal detail. …”
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Predicting rapid decline in kidney function among type 2 diabetes patients: A machine learning approach by Eri Nakahara, Kayo Waki, Hisashi Kurasawa, Imari Mimura, Tomohisa Seki, Akinori Fujino, Nagisa Shiomi, Masaomi Nangaku, Kazuhiko Ohe

“…Additionally, the model uses Recursive feature elimination with cross-validation (RFECV) to eliminate the features that do not contribute to the prediction. …”
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Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion by Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…We employed a two-stage machine learning approach: first applying Recursive Feature Elimination with multiple linear regression to identify core predictive items for total depression scores, followed by logistic regression for optimizing depression classification (CES-D ≥ 16). …”
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Machine learning based prediction of cognitive metrics using major biomarkers in SuperAgers by Hyo-Bin Lee, So-Yeon Kwon, Ji-Hae Park, Bori Kim, Geon-Ha Kim, Jang-Hwan Choi, Young Mi Park

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LENet: A Lightweight and Efficient High-Resolution Network for Human Pose Estimation by Ming Zhang, Xiandong Yu, Wenqiang Li, Xin Shu, Lei Pan, Zhongwei Shen

“…Due to the huge requirements of performing human pose estimation tasks on edge devices with limited resources, more and more researchers have turned to work on the design of lightweight human pose estimation networks. …”
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Feature-Driven EnsembleX: An Advanced Ensemble Framework for Enhanced MRI Abdomen Image Classification Using Feature Refinement and Boosting Techniques by Snehal V. Laddha, Rohini S. Ochawar

“…To refine the extracted features, Principal Component Analysis (PCA) was used to reduce dimensionality, followed by Recursive Feature Elimination (RFE) to select the most relevant attributes for classification. …”
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Reactor fault diagnosis based on common feature of multivariate vibration sequences by FU Ming, ZHU Ming, MEI Jie, ZHANG Jing, XIAO Li, ZHANG Zongxi

“…Aiming at the limitation that current feature selection algorithms are only available for univariate vibration sequence, this paper proposes a multivariate vibration sequences feature selection algorithm named SVM-RFE-GA based on support vector machine recursive feature elimination algorithm (SVM-RFE) and genetic algorithm (GA). …”
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Improved Online Kalman Smoothing Method for Ship Maneuvering Motion Data Using Expectation-Maximization Algorithm by Wancheng Yue, Junsheng Ren

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Dynamic feature selection for silicon content prediction in blast furnace using BOSVRRFE by Junyi Duan

“…This study proposes a Bayesian online sequential update and support vector regression recursive feature elimination (BOSVRRFE) algorithm for dynamic feature selection. …”
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Aerospace mold profile image feature optimization method based on trajectory planning by Chengwen Ma, Yilang Tu, Shuilin Rao, Dunguo Wu, Senlin Wang

“…We introduce brightness compensation and dynamic range limiting mechanisms and implement recursive fusion to optimize image quality. …”
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Discovering Key Successful Factors of Mobile Advertisements Using Feature Selection Approaches by Kai-Fu Yang, Venkateswarlu Nalluri, Chun-Cheng Liu, Long-Sheng Chen

“…Additionally, feature selection techniques have recently gained traction in big data analysis, addressing the limitations inherent in traditional statistical approaches. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. …”
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Optimal features assisted multi-attention fusion for robust fire recognition in adverse conditions by Inam Ullah, Nada Alzaben, Yousef Ibrahim Daradkeh, Mi Young Lee

“…Our approach introduces three key innovations: Firstly, Convolutional Self-Attention (CSA), integrating global self-attention with convolution through dynamic kernels and trainable filters for enhanced low-level fire feature processing. Secondly, Recursive Atrous Self-Attention (RASA) with optimized dilation rates, capturing comprehensive multi-scale contextual information through a recursive formulation with minimal parameter overhead. …”
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Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis by D. Dhinakaran, L. Srinivasan, S. Edwin Raja, K. Valarmathi, M. Gomathy Nayagam

“…The innovative architecture of SKR-DMKCF results in the reduction of dimensionality while preserving useful characteristics of the image utilizing recursive feature elimination and multi-kernel classification in a distributed environment. …”
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Study on Quantitative Evaluation Method of Balance Ability in Cancer Patients Based on Gait Features by Junjie LIU, Xu ZHOU, Chao YU, Qingqing CAO, Zhiming YAO, Wanqiu ZHANG, Ling ZHANG, Wanqing YAO, Ning LIN

“…Recursive feature elimination was used for feature selection. …”
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AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models by Faruk Dikmen, Ahmet Demir, Bestami Özkaya, Muhammad Owais Raza, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai

“…This study evaluates the performance of machine learning models in predicting key wastewater effluent parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen and Total Effluent Phosphorus. Three feature selection techniques were applied: SelectKBest, Mutual Information, and Recursive Feature Elimination (RFE) using Random Forest to identify the most significant predictors. …”
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