Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

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Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications by Fangling Wang, Azlan Mohd Zain, Yanjie Ren, Mahadi Bahari, Azurah A. Samah, Zuraini Binti Ali Shah, Norfadzlan Bin Yusup, Rozita Abdul Jalil, Azizah Mohamad, Nurulhuda Firdaus Mohd Azmi

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A Comprehensive Analysis of Feature Selection and Classification Techniques for Web Attack Classification by Zaman Ahmed Razak Abdul Ali

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A New Hybrid PSO-HHO Wrapper Based Optimization for Feature Selection by Sumbul Azeem, Shazia Javed, Iftikhar Naseer, Oualid Ali, Taher M. Ghazal

Subjects: “…Feature selection…”
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Adaptive Particle Swarm Optimization with Landscape Learning for Global Optimization and Feature Selection by Khalil Abbal, Mohammed El-Amrani, Oussama Aoun, Youssef Benadada

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An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring by Tales Boratto, Heder Soares Bernardino, Alex Borges Vieira, Tiago Silveira Gontijo, Matteo Bodini, Dmitriy A. Martyushev, Camila Martins Saporetti, Alexandre Cury, Flávio Barbosa, Leonardo Goliatt

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Wind speed forecasting approach using conformal prediction and feature importance selection by Cesar Vinicius Zuege, Stefano Frizzo Stefenon, Cristina Keiko Yamaguchi, Viviana Cocco Mariani, Gabriel Villarrubia Gonzalez, Leandro dos Santos Coelho

“…The proposed method considers the conformal prediction approach and, based on Shapley values, uses optimal selection of features given their importance. Furthermore, a Bayesian Optimization with Tree-structured Parzen Estimators (BO-TPE) will be used to tune the hyperparameters of the models. …”
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Feature selection methods for identifying genetic determinants of host species in RNA viruses. by Ricardo Aguas, Neil M Ferguson

“…RNA viruses pose an interesting case study given their mutation rates are orders of magnitude higher than any other pathogen--as reflected by the recent emergence of SARS and Influenza for example. Here, we show how feature selection techniques can be used to reliably classify viral sequences by host species, and to identify the crucial minority of host-specific sites in pathogen genomic data. …”
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Discovering Key Successful Factors of Mobile Advertisements Using Feature Selection Approaches by Kai-Fu Yang, Venkateswarlu Nalluri, Chun-Cheng Liu, Long-Sheng Chen

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Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity by Bingsheng Chen, Huijie Chen, Mengshan Li

“…The swarm intelligence algorithm simulates the behavior of animal populations in nature and is a new type of intelligent solution that is different from traditional artificial intelligence. Feature selection is a very common data dimensionality reduction method, which requires us to select the feature subset with the best evaluation criteria from the original feature set. …”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Adaptive dual-graph learning joint feature selection for EEG emotion recognition by Liangliang Hu, Congming Tan, Yin Tian

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Feature Selection and Ranking Method based on Intuitionistic Fuzzy Matrix and Rough Sets by Bich Khue Vo, Hung Son Nguyen

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Feature selection‐based android malware adversarial sample generation and detection method by Xiangjun Li, Ke Kong, Su Xu, Pengtao Qin, Daojing He

“…Using the frequency differential enhancement feature selection algorithm to perform feature screening, the algorithm forms two different feature sets and establishes two different training sets to train different classification algorithms. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

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A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification by Chellamuthu Gunavathi, Kandasamy Premalatha

“…Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. …”
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Employee Turnover Prediction Model Based on Feature Selection and Imbalanced Data Handling by Yuan Fang, Zhongqiu Zhang

“…The dataset underwent rigorous preprocessing and exploratory data analysis (EDA) to identify key patterns and relationships. Feature selection was performed using correlation matrix analysis, Chi-Square tests, and Recursive Feature Elimination (RFE) to identify the most relevant features. …”
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Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis by Agnieszka Wosiak, Danuta Zakrzewska

“…Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. …”
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Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection by Deepak Dudeja, Ajit Noonia, S. Lavanya, Vandana Sharma, Varun Kumar, Sumaiya Rehan, R. Ramkumar

“…The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. …”
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The impact of feature selection techniques on effort‐aware defect prediction: An empirical study by Fuyang Li, Wanpeng Lu, Jacky Wai Keung, Xiao Yu, Lina Gong, Juan Li

“…Previous studies indicated that some feature selection methods could improve the performance of Classification‐Based Defect Prediction (CBDP) models, and the Correlation‐based feature subset selection method with the Best First strategy (CorBF) performed the best. …”
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