A Fourfold Bi-Filter and Permuted Bi-Quad-Wrapper Feature Selection Method for Finding Optimal Moments of Multi-Trajectory Transient Records in Transient Analysis by Seyed Alireza Bashiri Mosavi, Omid Khalaf Beigi

Subjects: “…Filter-wrapper-based feature selection scheme…”
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Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy by Watshara Shoombuatong, Nalini Schaduangrat, Nutta Homdee, Saeed Ahmed, Pramote Chumnanpuen

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A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE) by Rebwar M. Nabi, Soran Ab. M. Saeed, Habibollah Harron

Subjects: “…Stock Market Forecasting, Feature Engineering Feature Selection Machine Learning Predictive Analysis Predictable Movement Multiclass Classification…”
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Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets by Kubra Noor, Ubaida Fatima

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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

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Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost by Deni Kurnia, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, Friska Abadi

“…The model with feature selection gets an AUC value of 0.9483, while the model without feature selection only gets an AUC value of 0.9366. …”
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Seleksi Fitur Menggunakan Hybrid Binary Grey Wolf Optimizer untuk Klasifikasi Hadist Teks Arab by M. Bahrul Subkhi, Chastine Fatichah, Agus Zaenal Arifin

“…This study proposes a feature selection method using the Hybrid Binary Gray Wolf Optimizer for Arabic text hadith classification. …”
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Kombinasi Seleksi Fitur Berbasis Filter dan Wrapper Menggunakan Naive Bayes pada Klasifikasi Penyakit Jantung by Siti Roziana Azizah, Rudy Herteno, Andi Farmadi, Dwi Kartini, Irwan Budiman

“…The purpose of this study is to determine the comparison of the accuracy results of Naive Bayes using several feature selections, namely Forward Selection, Backward Elimination, a combination of union of Forwad Selection and Backward Elimination feature selection results, Information Gain, Gain Ratio, and a combination of union of Information Gain feature selection results with Gain Ratio. …”
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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network by Guimei Yin, Jie Yuan, Yanjun Chen, Guangxing Guo, Dongli Shi, Lin Wang, Zilong Zhao, Yanli Zhao, Manjie Zhang, Yuan Dong, Bin Wang, Shuping Tan

“…This adaptive approach eliminates the human-specified criteria for feature selection and brain network construction. The trial results demonstrated that, when using a 6-second segment length and time-domain and frequency-domain features, patients with first-episode schizophrenia achieved the highest classification accuracy of 87.64% This method outperforms other feature selection and brain network modeling approaches, providing new insights and directions for the early diagnosis and recognition of schizophrenia.…”
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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. …”
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Delving into biomarkers and predictive modeling for CVD mortality: a 20-year cohort study by Zhen Wu, Abdullahi Mohamud Hilowle, Ying Zhou, Changlin Zhao, Shuo Yang

“…This study aims to develop a predictive model for CVD-related mortality using a machine learning-based feature selection algorithm and assess its performance compared to existing models. …”
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Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…The research results show that feature selection and class balancing have a positive impact. …”
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Retinopathy Diabetic Recognition and Detection Using Novel Intelligent Algorithms by Liaquat Ali Rahoo

“…The proposed approach consists of four levels: pre-processing for noise removal and standardization of the input dataset, image segmentation using Spiking Neural Network (SNN) based on edge detection, dimension reduction and feature selection using percolation theory, and the final step of combining SNN and percolation theory for retinopathy area detection. …”
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Penyeimbangan Kelas SMOTE dan Seleksi Fitur Ensemble Filter pada Support Vector Machine untuk Klasifikasi Penyakit Liver by Muhammad Amir Nugraha, Muhammad Itqan Mazdadi, Andi Farmadi, Muliadi, Triando Hamonangan Saragih

“…The test can prove if SMOTE on class balancing and Ensemble Filter on feature selection can improve the classification performance of the SVM method.…”
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Capsule neural network and adapted golden search optimizer based forest fire and smoke detection by Luling Liu, Li Chen, Mehdi Asadi

“…Testing this model on wildfire smoke imagery and the BowFire dataset reveals that the proposed methodology outperformed traditional feature selection and classification methods. The integration of the modified CNN and AGSO facilitated rapid response and mitigation efforts, enhancing the accuracy and dependability of forest fire identification. …”
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Federated Learning-Based Load Forecasting for Energy Aggregation Service Providers by HUANG Yichuan, SONG Yuhui, JING Zhaoxia

“…First，an artificial neural network with multidimensional environmental feature selection is established according to the task requirements. …”
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Prediction of inhibitory peptides against E.coli with desired MIC value by Nisha Bajiya, Nishant Kumar, Gajendra P. S. Raghava

“…Subsequently, we employed machine learning regression models that integrated various features, including peptide composition, binary profiles and embeddings from large language models. Feature selection techniques, particularly mRMR, were utilized to refine our model inputs. …”
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Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability by Mahmoud AlJamal, Rabee Alquran, Ayoub Alsarhan, Mohammad Aljaidi, Wafa’ Q. Al-Jamal, Ali Fayez Alkoradees

“…Leveraging the CIC Truth Seeker Dataset 2023, we applied SHAP for feature selection and interpretability, ensuring transparency in the model’s predictions. …”
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An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The problem statement of this research paper revolves around addressing challenges concerning feature selection and evolving spammer behavior and linguistic features, with the goal of devising an efficient model for spam detection. …”
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An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Twum Frimpong, Abdul-Salaam Gaddafi

“…This is achieved by adopting effective feature selection technique to estimate variable relationships with the target variable. …”
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