Methane Concentration Inversion Based on Multi-Feature Fusion and Stacking Integration by Yanling Han, Wei Li, Congqin Yi, Ge Song, Yun Zhang

“…The method leverages the series-parallel cascade structure between multiple base models and meta-models to learn different feature representations and patterns in the original data, fully exploring the intrinsic relationships between various feature factors and methane concentration. …”
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Emotion Recognition in the Eye Region Using Textural Features, IBP and HOG by Laura Jalili, Josue Espejel, Jair Cervantes, Farid Lamont

“…Results: Our experimental methodology involved employing various classification techniques toassess performance across different models. Among these, SVM exhibited exceptional performance, boasting an impressive accuracy rate of 99.2 %. …”
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Feature Engineering for the Prediction of Scoliosis in 5q‐Spinal Muscular Atrophy by Tu‐Lan Vu‐Han, Vikram Sunkara, Rodrigo Bermudez‐Schettino, Jakob Schwechten, Robin Runge, Carsten Perka, Tobias Winkler, Sebastian Pokutta, Claudia Weiß, Matthias Pumberger

“…To test the predictive performance of the selected features, we trained a Random Forest Classifier and evaluated model performance using standard metrics. …”
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In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning by Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, Nasem Badreldin

“…This study evaluated the performance of three machine learning (ML) models—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR)—for predicting potato N status and examined the impact of feature selection techniques, including Partial Least Squares Regression (PLSR), Boruta, and Recursive Feature Elimination (RFE). …”
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SAR Target Recognition With Image Generation and Azimuth Angle Feature Constraints by Deliang Xiang, Ye Liu, Jianda Cheng, Xinyu Lu, Yuzhen Xie, Dongdong Guan

“…Experiments conducted on the moving and stationary target acquisition and recognition and a self-collected dataset demonstrate that our method outperforms existing baselines in structural detail preservation and feature distribution quality. When the generated data are used to retrain the recognition model, target recognition accuracy improves by 1.69% and 3.03%, respectively, validating the effectiveness of the proposed generation strategy in boosting downstream automatic target recognition performance.…”
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A Linguistic Features-Based Approach for the Functional Analysis of Disinformation in Spanish by Eduardo Puraivan, Fabian Riquelme, Rene Venegas

“…Moreover, these models demonstrate strong performance when the same features are applied to a different dataset and continue to perform well when the feature selection is adjusted to fit the new context.…”
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Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization by Yifan Shi, Haixin Zeng, Xinrong Gong, Lei Cai, Wenjie Xiang, Qi Lin, Huijie Zheng, Jianqing Zhu

“…A hybrid regularization strategy incorporating the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm and the Frobenius norm is introduced into the feature selection objective function, which not only promotes feature sparsity but also effectively prevents overfitting, thereby improving the stability of the model. …”
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Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification by Zongqian Zhan, Zirou Xiong, Xin Huang, Chun Yang, Yi Liu, Xin Wang

“…In addition, compared with other state-of-the-art models, our method can achieve similar or even better classification results, yet offer superior inference performance.…”
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Features of fault transient's in generator network of powerful electric power stations by E. P. Safonov, V. Ya. Frolov

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Feature selection based on Mahalanobis distance for early Parkinson disease classification by Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ahmed M. Mahdi, Ayman Ibaida

“…Significant improvements in classification performance were observed across all models. On the ''Parkinson Disease Classification Dataset'', the feature set was reduced from 22 to 11 features, resulting in accuracy improvements ranging from 10.17 % to 20.34 %, with the K-Nearest Neighbors (KNN) classifier achieving the highest accuracy of 98.31 %. …”
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Adaptive deep feature representation learning for cross-subject EEG decoding by Shuang Liang, Linzhe Li, Wei Zu, Wei Feng, Wenlong Hang

“…Methods: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. …”
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Two-level feature selection method based on SVM for intrusion detection by Xiao-nian WU, Xiao-jin PENG, Yu-yang YANG, Kun FANG

“…To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively.…”
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YOLO-SWD—An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios by Ruyan Zhou, Mingkang Gu, Haiyan Pan

“…This study aims to enhance the accuracy and robustness of ship recognition by improving deep learning-based object detection models, enabling the algorithm to perform ship detection and recognition tasks effectively in feature-occluded scenarios. …”
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Research on filter-based adversarial feature selection against evasion attacks by Qimeng HUANG, Miaomiao WU, Yun LI

“…With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification, resulting in serious consequences in various domains such as spam detection, traffic signal recognition, and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed, based on the minimum redundancy and maximum relevance (mRMR) method, while considering security metrics against evasion attacks.Additionally, a robust adversarial feature selection algorithm was introduced, named SDPOSS, which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases, the runtime of SDPOSS decreases linearly, while achieving excellent classification performance.Moreover, SDPOSS exhibits strong robustness against evasion attacks, providing new insights for adversarial machine learning.…”
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CNN-based salient features in HSI image semantic target prediction by Vishal Srivastava, Bhaskar Biswas

“…Therefore in this work, we have extracted the informative features from different CNN models for the benchmark HSI datasets. …”
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Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

“…Using the MIMIC-IV dataset, we employ the Wolverine Optimization Algorithm (WoOA) to select clinically relevant features, followed by a Generative Pre-Training Graph Neural Network (GPT-GNN) that models complex patient relationships through self-supervised learning. …”
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Keywords, morpheme parsing and syntactic trees: features for text complexity assessment by Dmitry A. Morozov, Ivan A. Smal, Timur A. Garipov, Anna V. Glazkova

“…The use of an extensive set of syntactic features allowed, in most cases, to improve the quality of work of neural network models in comparison with the previously described set.…”
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Automated prediction of fibroblast phenotypes using mathematical descriptors of cellular features by Alex Khang, Abigail Barmore, Georgios Tseropoulos, Kaustav Bera, Dilara Batan, Kristi S. Anseth

“…We train and validate models on features extracted from over 3000 primary heart valve interstitial cells and test their predictive performance on cells treated with the small molecule drugs 5-azacytidine and bisperoxovanadium (HOpic), which inhibited and promoted myofibroblast activation, respectively. …”
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Physically Based Dimensionless Features for Pluvial Flood Mapping With Machine Learning by Mark S. Bartlett, Jared VanBlitterswyk, Martha Farella, Jinshu Li, Curtis Smith, Anthony J. Parolari, Lalitha Krishnamoorthy, Assaad Mrad

“…This is demonstrated by incorporating them as features in a logistic regression model for delineating flood extents. …”
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An Indoor Scene Classification Method for Service Robot Based on CNN Feature by Shaopeng Liu, Guohui Tian

“…To further evaluate our method, test experiments on unknown scene images from SUN 397 dataset had been done, and the models based on different training datasets obtained 94.34% and 79.80% test accuracy severally, which proved that the proposed method owned good performance in indoor scene classification.…”
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