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  1. 181

    Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset by Ines Aulia Latifah, Fauzi Adi Rafrastara, Jevan Bintoro, Wildanil Ghozi, Waleed Mahgoub Osman

    Published 2024-11-01
    “…This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. …”
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    Article
  2. 182

    Feature selection in single-cell RNA sequencing data: a comprehensive evaluation by Petros Paplomatas, Konstantinos Lazaros, Georgios N. Dimitrakopoulos, Aristidis Vrahatis

    Published 2024-09-01
    “…We developed the GenesRanking package, which offers 20 techniques for dimensionality reduction, including filter-based and embedding machine learning–based methods. By integrating feature selection methods from both statistics and machine learning, we provide a robust framework for improving data interpretation. …”
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    Article
  3. 183

    Optimal Statistical Feature Subset Selection for Bearing Fault Detection and Severity Estimation by Chhaya Grover, Neelam Turk

    Published 2020-01-01
    “…The performance of bearing fault detection systems based on machine learning techniques largely depends on the selected features. Hence, selection of an ideal number of dominant features from a comprehensive list of features is needed to decrease the number of computations involved in fault detection. …”
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    Efficient feature selection for histopathological image classification with improved multi-objective WOA by Ravi Sharma, Kapil Sharma, Manju Bala

    Published 2024-10-01
    “…Abstract The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. …”
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    Heart Disease Diagnostics Using Meta-Learning-Based Hybrid Feature Selection by Kaushalya Dissanayake, Md Gapar Md Johar

    Published 2024-01-01
    “…This study introduces an innovative approach to diagnosing heart disease by combining classifiers in a meta-learning-based approach and utilizing advanced feature selection methods in a hybrid model. Using the extensive heart disease dataset provided by the IEEE DataPort, our method aims to enhance the accuracy of diagnosis by gradually refining the selection of relevant features at two separate stages. …”
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    Article
  13. 193

    Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance by Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng, Alessandro Laio

    Published 2025-01-01
    “…Abstract Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? …”
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  14. 194

    Deconstructability prediction for building using machine learning and ensemble feature selection techniques by Habeeb Balogun, Hafiz Alaka, Eren Demir, Christian Nnaemeka Egwim, Godoyon Ebenezer Wusu, Wasiu Yusuf, Muideen Adegoke, Iqbal Qasim

    Published 2025-07-01
    “…A deconstructability predictive model using a machine learning-based model and ensemble feature selection techniques was developed to tackle this problem. …”
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    Article
  15. 195

    Random forest–based feature selection and detection method for drunk driving recognition by ZhenLong Li, HaoXin Wang, YaoWei Zhang, XiaoHua Zhao

    Published 2020-02-01
    “…A method for drunk driving detection using Feature Selection based on the Random Forest was proposed. …”
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    A composite Feature Selection Method to improve Classifying Imbalanced Big Data by Shaymaa Razoqi, Ghayda Al-Talib

    Published 2024-12-01
    “…Therefore, this research proposed a composed feature selection method using the filter feature selection technique and permutation-based important features with the ensemble learning method. …”
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