Detecting respiratory diseases using machine learning-based pattern recognition on spirometry data by Ahmed I. Taloba, R.T. Matoog

“…Due to issues with dimensionality and computational complexity, the relevant features are selected using Forward Feature Selection (FFS). The classification approach synthesizes two methods, support vector machines, and k-nearest neighbors, to reveal intricate patterns and boundaries in the data. …”
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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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Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

“…Recursive Feature Elimination (RFE) was employed for feature selection, and we trained seven supervised classifiers. …”
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Public Housing Allocation Model in the Guangdong-Hong Kong-Macao Greater Bay Area under Clustering Algorithm by Lei Zhang, Xueqing Hu

“…The simulation experiments are performed on the clustering algorithm optimized based on rough set feature selection. On the Chess data set, the optimized clustering algorithm shows an obvious improvement in clustering accuracy and recall rate compared with the traditional clustering algorithms, which are 0.76 and 0.95, respectively. …”
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Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa by Takhamo Gori, Andi Sunyoto, Hanif Al Fatta

“…Penelitian ini bertujuan untuk memprediksi kinerja akademik siswa dengan mengintegrasikan metode Correlation-Based Feature Selection (CFS) dan Algoritma Naïve Nayes pada gabungan dataset pelajaran Matematika dan Bahasa Portugis dua sekolah menengah di Portugal. …”
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A hybrid machine learning approach for the personalized prognostication of aggressive skin cancers by Tom W. Andrew, Mogdad Alrawi, Ruth Plummer, Nick Reynolds, Vern Sondak, Isaac Brownell, Penny E. Lovat, Aidan Rose, Sophia Z. Shalhout

“…We then combined deep learning feature selection with a modified XGBoost framework, to develop a web-based prognostic tool for MCC termed ‘DeepMerkel’. …”
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Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations by Nohyeong Jeong, Shinyun Park, Subhamoy Mahajan, Ji Zhou, Jens Blotevogel, Ying Li, Tiezheng Tong, Yongsheng Chen

“…The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency.…”
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CAT-RFE: ensemble detection framework for click fraud by Yixiang LU, Guanggang GENG, Zhiwei YAN, Xiaomin ZHU, Xinchang ZHANG

“…Click fraud is one of the most common methods of cybercrime in recent years, and the Internet advertising industry suffers huge losses every year because of click fraud.In order to effectively detect fraudulent clicks within massive clicks, a variety of features that fully combine the relationship between advertising clicks and time attributes were constructed.Besides, an ensemble learning framework for click fraud detection was proposed, namely CAT-RFE ensemble learning framework.The CAT-RFE ensemble learning framework consisted of three parts: base classifier, recursive feature elimination (RFE) and voting ensemble learning.Among them, the gradient boosting model suitable for category features-CatBoost was used as the base classifier.RFE was a feature selection method based on greedy strategy, which can select a better feature combination from multiple sets of features.Voting ensemble learning was a learning method that combined the results of multiple base classifiers by voting.The framework obtained multiple sets of optimal feature combinations in the feature space through CatBoost and RFE, and then integrated the training results under these feature combinations through voting to obtain integrated click fraud detection results.The framework adopted the same base classifier and ensemble learning method, which not only overcame the problem of unsatisfactory integrated results due to the mutual constraints of different classifiers, but also overcame the tendency of RFE to fall into a local optimal solution when selecting features, so that it had better detection ability.The performance evaluation and comparative experimental results on the actual Internet click fraud dataset show that the click fraud detection ability of the CAT-RFE ensemble learning framework exceeds that of the CatBoost method, the combined method of CatBoost and RFE, and other machine learning methods, proving that the framework has good competitiveness.The proposed framework provides a feasible solution for Internet advertising click fraud detection.…”
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A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier by Zhuokai Jian, Bin Ai, Jiali Zeng, Yuchao Sun

“…Specifically, the method utilized a small sample size to successfully obtain pure and accurate mangrove classification. The feature selection indicates that the short-wave infrared band and its associated remote sensing indices play a pivotal role in differentiating mangroves from other confusable land classes. …”
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Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model by Vishan Kumar Gupta, Avdhesh Gupta, Dinesh Kumar, Anjali Sardana

“…On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine, decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. …”
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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach by Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan

“…The preprocessing stage employs normalization, class balancing with synthetic oversampling, and five feature selection techniques, including RFECV and Pearson Correlation. …”
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Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning-Based Image Analysis by J. Jerome Vasanth, S. Naveen Venkatesh, V. Sugumaran, Vetri Selvi Mahamuni

“…During the machine learning phase, feature selection from the extracted features was carried out using the J48 decision tree algorithm. …”
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Breast Cancer Prediction: A Fusion of Genetic Algorithm, Chemical Reaction Optimization, and Machine Learning Techniques by Md. Rafiqul Islam, Md. Shahidul Islam, Saikat Majumder

“…GA and CRO are used to optimize the feature selection process. It enables machine learning algorithms to predict more accurately. …”
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Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks by Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin, MooHyun Kim

“…Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“…By utilizing differential expression analysis (DEG) with Linear Models for Microarray (LIMMA) and LASSO regression for feature selection and leveraging graph attention networks (GATs) to incorporate protein–protein interaction (PPI) networks, LASSO–MOGAT effectively captures intricate relationships within multi-omics data. …”
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A Multi-Branch Anchor-Free Detection Algorithm for Hospital Pedestrian by Keqiang Li, Yifan Li, Yiyi Wang, Haining Yu, Huan Zhang

“…Lastly, a local feature selection network is proposed to adaptively suppress non-optimal values from the multi-branch outputs, eliminating redundant feature boxes during prediction. …”
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Age group classification based on optical measurement of brain pulsation using machine learning by Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä

“…ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. Performance with increasing sample size was estimated using learning curve method. …”
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Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events by R. E. Rolón, I. E. Gareis, L. E. Di Persia, R. D. Spies, H. L. Rufiner

“…In the context of feature selection problems, several complexity-based measures have been proposed. …”
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Development of Hybrid Intrusion Detection System Leveraging Ensemble Stacked Feature Selectors and Learning Classifiers to Mitigate the DoS Attacks by P. Mamatha, S. Balaji, S. Sai Anuraghav

“…To tackle this aforementioned problem, this research article presents the hybrid IDS based on the combination of stacked feature selection methods such as Random Boruta Selector (RFS), Relief, Pearson coefficient (PCE) and Stacked learning classifiers (SLF). …”
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