Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms by Rivindu Weerasekera, Mohan Sridharan, Prakash Ranjitkar

“…The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.…”
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Enhancing thyroid disease prediction and comorbidity management through advanced machine learning frameworks by P. Sanju, N. Syed Siraj Ahmed, P. Ramachandran, P. Mohamed Sajid, R. Jayanthi

“…This study proposes a Hybrid Feature Selection and Deep Learning Framework (HFSDLF) that integrates Random Forests with Principal Component Analysis (PCA) and L1 regularization for effective feature selection and classification. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…By utilizing an attention mechanism for dynamic feature selection and weighting, the robustness of classification is further improved. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. …”
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A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables by Hua Li, Deyu Li, Yanhui Zhai, Suge Wang, Jing Zhang

“…Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. …”
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On Facial Expression Recognition Benchmarks by Ebenezer Owusu, Jacqueline Asor Kumi, Justice Kwame Appati

“…We selected published works from 2010 to 2021 and extracted, analyzed, and summarized the findings based on the most used techniques in feature extraction, feature selection, validation, databases, and classification. …”
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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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Comparative analysis of regression algorithms for drug response prediction using GDSC dataset by Soojung Ha, Juho Park, Kyuri Jo

“…Three analyses was conducted to show the effect of feature selection methods, multiomics information, and drug categories on drug response prediction. …”
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Analysis of Emotional Stress of Teachers in Japanese Teaching Process Based on EEG Signal Analysis by Jie Dong

“…An experiment of teachers’ emotional stress relief recognition in Japanese teaching process based on EEG signal was designed. The feature selection algorithm was used to screen the EEG feature combinations of teachers’ emotional stress relief and healthy subjects, and the classification experiment was carried out to verify the difference. …”
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Effects of Pooling Samples on the Performance of Classification Algorithms: A Comparative Study by Kanthida Kusonmano, Michael Netzer, Christian Baumgartner, Matthias Dehmer, Klaus R. Liedl, Armin Graber

“…We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. …”
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PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost by Fei Guo, Zhixiang Yin, Kai Zhou, Jiasi Li

“…There have been a large number of identification tools based on machine-learning and deep learning algorithms, mostly using human and mouse gene sequences as training sets, seldom plants, and only using one or one class of feature selection methods after feature extraction. We developed an identification model containing dicot, monocot, algae, moss, and fern. …”
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AccFIT-IDS: accuracy-based feature inclusion technique for intrusion detection system by C. Rajathi, P. Rukmani

“…To address this, the study proposed AccFIT (Accuracy-based Feature Inclusion Technique) for IDS, combining two-stage Feature Selection Algorithms (FSA). In stage 1, various filter-based feature selection methods are applied extract features from intial dataset. …”
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A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems by Li Duan, Jingxian Zhou, You Wu, Wenyao Xu

“…Then, the autoencoder neural network for feature selection is used to improve the efficiency of model construction. …”
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Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital by Mohammad Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh, Yara Alkayed, Yasmin Al Trabsheh, Bahaaldeen Alshraideh

“…The primary objective of this study is to enhance prediction accuracy by utilizing a comprehensive approach that includes data preprocessing, feature selection, and model development. Various artificial intelligence techniques, namely, random forest, SVM, decision tree, naive Bayes, and K-nearest neighbours (KNN) were explored with particle swarm optimization (PSO) for feature selection. …”
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Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach by Siti Aminah, Gianinna Ardaneswari, Mufarrido Husnah, Ghani Deori, Handi Bagus Prasetyo

“…This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. …”
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Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms by Liu　Yuxiao, Dong　Yanwu, Jiang　Zhouhua, Chen　Xi

“…The MI method is utilized for feature selection and assessment of factors affecting the endpoint phosphorus content. …”
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A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals by Mohammad Amin Shayegan, Saeed Aghabozorgi, Ram Gopal Raj

“…Dimensionality reduction (feature selection) is an important step in pattern recognition systems. …”
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Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm. by Zongjin Li, Liqin Tian, Libing Bai, Zeyu Jia, Xiaoming Wu, Changxin Song

“…Finally, it combines integrated feature selection methods to determine the gene biomarkers. …”
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Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India by B. Suresh Kumar, Jenifer Mahilraj, R. K. Chaurasia, Chitaranjan Dalai, A. H. Seikh, S. M. A. K. Mohammed, Ram Subbiah, Abdi Diriba

“…There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. …”
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