A survey on feature selection methods

Particle swarm optimization

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Genetic Algorithms

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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

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Gene selection for cancer classification using support vector machines

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Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification

Wang, Z., Gao, S., Zhang, Y., & Guo, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109874 · Cited by 38

Detecting code smells using industry-relevant data

Madeyski, L., & Lewowski, T. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.107112 · Cited by 38

Breath VOC analysis and machine learning approaches for disease screening: a review

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D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System

Lee, J.-H., Tang, F., Ye, P., Abbasi, F. H., Hay, P., & Divakaran, D. M. (2021) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp51992.2021.00045 · Cited by 43

Textual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification

Gite, S., Patil, S., Dharrao, D., Yadav, M., Basak, S., Rajendran, A., & Kotecha, K. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010045 · Cited by 37

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Saleem, M., Aslam, W., Lali, M. I. U., Rauf, H. T., & Nasr, E. A. (2023) · Diagnostics · doi:10.3390/diagnostics13223441 · Cited by 38

An Integrated Framework of Two-Stream Deep Learning Models Optimal Information Fusion for Fruits Disease Recognition

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Novel Machine Learning Based Credit Card Fraud Detection Systems

Feng, X., & Kim, S.-K. (2024) · Mathematics · doi:10.3390/math12121869 · Cited by 37

Federated continual learning: Concepts, challenges, and solutions

Hamedi, P., Razavi‐Far, R., & Hallaji, E. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130844 · Cited by 55

Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review

Ding, L., Zhang, C., Yue, Y., Yao, C., Li, Z., Hu, Y., Yang, B., Ma, W., Li, Q., Yu, L., et al. (2025) · Sensors · doi:10.3390/s25144515 · Cited by 37

Feature selection in multimedia: The state-of-the-art review

Lee, P. Y., Loh, W. P., & Chin, J. F. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.09.004 · Cited by 37

Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection

Dowlatshahi, M. B., Derhami, V., & Nezamabadi–pour, H. (2017) · Information · doi:10.3390/info8040152 · Cited by 37

Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma

Singh, N., Bapi, R. S., & Vinod, P. K. U. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.06.030 · Cited by 40

Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

Tan, M. S., Chang, S., Cheah, P., & Yap, H. J. (2018) · PeerJ · doi:10.7717/peerj.5285 · Cited by 37

A Study on the Relevance of Feature Selection Methods in Microarray Data

Sahu, B., Dehuri, S., & Jagadev, A. K. (2018) · The Open Bioinformatics Journal · doi:10.2174/1875036201811010117 · Cited by 37

Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems

Sheikhpour, R., Sarram, M. A., & Sheikhpour, E. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.035 · Cited by 37

Methods of Authentication of Food Grown in Organic and Conventional Systems Using Chemometrics and Data Mining Algorithms: a Review

de Lima, M. D., & Barbosa, R. (2019) · Food Analytical Methods · doi:10.1007/s12161-018-01413-3 · Cited by 39

MOKBL+MOMs: An interpretable multi-objective evolutionary fuzzy system for learning high-dimensional regression data

Aghaeipoor, F., & Javidi, M. M. (2019) · Information Sciences · doi:10.1016/j.ins.2019.04.035 · Cited by 37

Automated classification of social network messages into Smart Cities dimensions

Bencke, L. R., Cechinel, C., & Muñoz, R. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.03.057 · Cited by 37

Novel and efficient randomized algorithms for feature selection

Wang, Z., Xiao, X., & Rajasekaran, S. (2020) · Big Data Mining and Analytics · doi:10.26599/bdma.2020.9020005 · Cited by 37

Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

Isaac, A., Nehemiah, H. K., Isaac, A., & Kannan, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103940 · Cited by 37

A modified genetic algorithm and weighted principal component analysis based feature selection and extraction strategy in agriculture

Shastry, K. A., & Sanjay, H. A. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107460 · Cited by 37

An automated vibration-based structural damage localization strategy using filter-type feature selection

Alves, V., & Cury, A. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110145 · Cited by 37

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

Liang, R., Xie, J., Zhang, C., Zhang, M., Huang, H., Huo, H., Cao, X., & Niu, B. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666191016155543 · Cited by 36

Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach

Garabaghi, F. H., Benzer, S., & Benzer, R. (2023) · Environmental Monitoring and Assessment · doi:10.1007/s10661-023-11492-3 · Cited by 36

MIC-SHAP: An ensemble feature selection method for materials machine learning

Wang, J., Xu, P., Ji, X., Li, M., & Lu, W. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.106910 · Cited by 36

Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid

Munir, N., McMorrow, R., Mulrennan, K., Whitaker, D. A., McLoone, S., Kellomäki, M., Talvitie, E., Lyyra, I., & McAfee, M. (2023) · Polymers · doi:10.3390/polym15173566 · Cited by 36

Graph neural network for predicting the effective properties of polycrystalline materials: A comprehensive analysis

Dai, M., Demirel, M. F., Liu, X., Liang, Y., & Hu, J. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112461 · Cited by 36

Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage

Li, Z., Zhang, S., Huang, H., Liu, R., Cai, M., Bian, Y., Chang, L., & Du, H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108926 · Cited by 39

A centroid-based gene selection method for microarray data classification

Guo, S., Guo, D., Chen, L., & Jiang, Q. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.03.034 · Cited by 36

Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization

Liang, L., Liu, F., Li, M., He, K., & Xu, G. (2016) · Measurement · doi:10.1016/j.measurement.2016.08.003 · Cited by 36

Detection of malicious web pages based on hybrid analysis

Wang, R., Zhu, Y., Tan, J., & Zhou, B. (2017) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2017.05.008 · Cited by 36

Feature Selection Algorithm for Intrusions Detection System using Sequential Forward Search and Random Forest Classifier

Lee, J., Park, D., & Lee, C. (2017) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2017.10.024 · Cited by 36

Acoustic data condensation to enhance pipeline leak detection

Oh, S. W., Yoon, D.-B., Kim, G. J., Bae, J., & Kim, H. S. (2018) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2017.12.006 · Cited by 37

Evolutionary feature subspaces generation for ensemble classification

Zhang, B., Qin, A. K., & Sellis, T. (2018) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3205455.3205638 · Cited by 36

An Improved Binary Whale Optimization Algorithm for Feature Selection of Network Intrusion Detection

Xu, H., Fu, Y., Fang, C., Cao, Q., Su, J., & Wei, S. (2018) · International Symposium on Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS) · doi:10.1109/idaacs-sws.2018.8525539 · Cited by 36

Boosting decision stumps for dynamic feature selection on data streams

Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019) · Information Systems · doi:10.1016/j.is.2019.02.003 · Cited by 36

Multi-UAV Allocation Framework for Predictive Crime Deterrence and Data Acquisition

Miyano, K., Shinkuma, R., Shiode, N., Shiode, S., Sato, T., & Oki, E. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100205 · Cited by 36

High-throughput corn ear screening method based on two-pathway convolutional neural network

Zhang, J., Ma, Q., Cui, X., Guo, H., Wang, K., & Zhu, D. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105525 · Cited by 36

Predicting the concentration of total coliforms in treated rural domestic wastewater by multi-soil-layering (MSL) technology using artificial neural networks

Sbahi, S., Ouazzani, N., Latrach, L., Hejjaj, A., & Mandi, L. (2020) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2020.111118 · Cited by 36

Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities

Banga, A., Ahuja, R., & Sharma, S. C. (2021) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-020-01049-9 · Cited by 36

A quantum-inspired classifier for clonogenic assay evaluations

Sergioli, G., Militello, C., Rundo, L., Minafra, L., Torrisi, F., Russo, G. I., Chow, K. L., & Giuntini, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-82085-8 · Cited by 40

High-Performance Modelling and Simulation for Big Data Applications

Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37

A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms

Yıldırım, E., Kaya, Y., & Kılıç, F. (2021) · IEEE Access · doi:10.1109/access.2021.3100638 · Cited by 39

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika, C., & Selvamuthukumaran, S. (2021) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-021-00013-0 · Cited by 36

Sparse Index Tracking With K-Sparsity or ϵ-Deviation Constraint via ℓ 0 -Norm Minimization

Li, X. P., Shi, Z.-L., Leung, C.-S., & So, H. C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3171819 · Cited by 36

IGJO: An Improved Golden Jackel Optimization Algorithm Using Local Escaping Operator for Feature Selection Problems

Devi, R. M., Premkumar, M., Kiruthiga, G., & Sowmya, R. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11146-y · Cited by 36

Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features for High-Dimensional Classification

Tran, B., Xue, B., & Zhang, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31204-0_45 · Cited by 32

Predicting hERG channel blockers with directed message passing neural networks

Shan, M., Chen, J., Chen, J., Qin, L., Qin, J., & Cheng, G. (2022) · RSC Advances · doi:10.1039/d1ra07956e · Cited by 35

Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass

Huang, W., Li, W., Xu, J. M., Ma, X., Li, C., & Liu, C. (2022) · Remote Sensing · doi:10.3390/rs14092086 · Cited by 36

CCBLA: a Lightweight Phishing Detection Model Based on CNN, BiLSTM, and Attention Mechanism

Zhu, E., Yuan, Q., Chen, Z., Li, X., & Fang, X. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10024-4 · Cited by 35

Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection

Hu, H., Shan, W., Tang, Y., Heidari, A. A., Chen, H., Liu, H., Wang, M., Escorcia‐Gutierrez, J., Mansour, R. F., & Chen, J. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac119 · Cited by 35

How well do investor sentiment and ensemble learning predict Bitcoin prices?

Hájek, P., Hikkerova, L., & Sahut, J. (2022) · Research in International Business and Finance · doi:10.1016/j.ribaf.2022.101836 · Cited by 35

Cost-sensitive stacking ensemble learning for company financial distress prediction

Wang, S., & Chi, G. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124525 · Cited by 35

Autoencoder-Based fault detection using building automation system data

Mokhtari, K. E., & McArthur, J. J. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102810 · Cited by 37

Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm

Zhang, J., Gao, B., Chai, H., Ma, Z., & Yang, G. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1201-8 · Cited by 35

Selection of Robust and Relevant Features for 3-D Steganalysis

Li, Z., & Borş, A. G. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2883082 · Cited by 35

Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted From Machine Learning Model

Inagaki, Y., Shinkuma, R., Sato, T., & Oki, E. (2019) · IEEE Access · doi:10.1109/access.2019.2928216 · Cited by 35

Using Supervised Machine Learning in Automated Content Analysis: An Example Using Relational Uncertainty

Pilny, A. N., McAninch, K. G., Slone, A., & Moore, K. (2019) · Communication Methods and Measures · doi:10.1080/19312458.2019.1650166 · Cited by 35

Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

Samuel, O., Al-Zahrani, F. A., Khan, R. J. U. H., Farooq, H., Shafiq, M., Afzal, M. K., & Javaid, N. (2020) · Entropy · doi:10.3390/e22010068 · Cited by 35

Characterization of biomedical signals: Feature engineering and extraction

Rajoub, B. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818946-7.00002-0 · Cited by 35

A model-based health indicator for leak detection in gas pipeline systems

Xiao, R., Hu, Q., & Li, J. (2020) · Measurement · doi:10.1016/j.measurement.2020.108843 · Cited by 35

Optimal Selection of Features Using Artificial Electric Field Algorithm for Classification

Das, H., Naik, B., & Behera, H. S. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-05486-x · Cited by 35

Credit Decision Support Based on Real Set of Cash Loans Using Integrated Machine Learning Algorithms

Ziemba, P., Becker, J., Becker, A., Radomska-Zalas, A., Pawluk, M., & Wierzba, D. (2021) · Electronics · doi:10.3390/electronics10172099 · Cited by 35

Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up

Kumar, R. E., Tiihonen, A., Sun, S., Fenning, D. P., Liu, Z., & Buonassisi, T. (2022) · Matter · doi:10.1016/j.matt.2022.04.016 · Cited by 39

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

AlShammari, M. M., Almuhanna, A., & Alhiyafi, J. A. (2021) · Sensors · doi:10.3390/s22010203 · Cited by 35

Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest

Singh, M., & Huang, Z. (2022) · Sustainability · doi:10.3390/su14020992 · Cited by 38

Accurate identification of influential building parameters through an integration of global sensitivity and feature selection techniques

Neale, J., Shamsi, M. H., Mangina, E., Finn, D., & O’Donnell, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118956 · Cited by 35

LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications

Xin, J., Hwang, K., & Yu, Z. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3526157 · Cited by 35

Cuckoo Search-Based Optimization for Cancer Classification: A New Hybrid Approach

Aziz, R. M. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0410 · Cited by 35

Genomic Prediction of Wheat Grain Yield Using Machine Learning

Sirsat, M. S., Oblessuc, P. R., & Ramiro, R. S. (2022) · Agriculture · doi:10.3390/agriculture12091406 · Cited by 39

Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

Chadaga, K., Prabhu, S., Sampathila, N., Chadaga, R., Swathi, K. S., & Sengupta, S. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2143040 · Cited by 35

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

Nouri-Moghaddam, B., Ghazanfari, M., & Fathian, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06459-9 · Cited by 34

The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool

Gerchick, M. K., Jegede, T., Shah, T., Gutiérrez, A. B., Beiers, S., Shemtov, N., Xu, K., Samant, A., & Horowitz, A. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594081 · Cited by 41

A review of feature set partitioning methods for multi-view ensemble learning

Kumar, A., & Yadav, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101959 · Cited by 34

Wind power plants hybridised with solar power: A generation forecast perspective

Couto, A., & Estanqueiro, A. I. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.138793 · Cited by 34

Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization

Nssibi, M., Manita, G., Chhabra, A., Mirjalili, S., & Korbaa, O. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10675-1 · Cited by 34

Enhancement of tea leaf diseases identification using modified SOTA models

Panchbhai, K. G., & Lanjewar, M. G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10758-2 · Cited by 34

A hybrid multi-objective optimization approach with NSGA-II for feature selection

Vijai, P., & Sivakumar, P. B. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100550 · Cited by 39

Evolutionary Multitask Optimization for Multiform Feature Selection in Classification

Yang, Q.-T., Xu, X.-X., Zhan, Z., Zhong, J., Kwong, S., & Zhang, J. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3535722 · Cited by 34

Expertise Finding in Bibliographic Network: Topic Dominance Learning Approach

Neshati, M., Hashemi, S. H., & Beigy, H. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2312614 · Cited by 34

Determining appropriate approaches for using data in feature selection

Aldehim, G., & Wang, W. (2015) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-015-0469-8 · Cited by 34

Relief F-Based Feature Ranking and Feature Selection for Monitoring Induction Motors

Stief, A., Ottewill, J. R., & Baranowski, J. (2018) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2018.8486097 · Cited by 34

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Jawed, S., Amin, H. U., Malik, A. S., & Faye, I. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00086 · Cited by 34

Fault detection and identification combining process measurements and statistical alarms

Lucke, M., Stief, A., Chioua, M., Ottewill, J. R., & Thornhill, N. F. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.104195 · Cited by 34

A new machine learning method consisting of GA-LR and ANN for attack detection

Hosseini, S. (2020) · Wireless Networks · doi:10.1007/s11276-020-02321-3 · Cited by 34

An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

Kashif, M. I., Raja, G., & Shaukat, F. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-020-00338-w · Cited by 34

Ensemble of ML-KNN for classification algorithm recommendation

Zhu, X., Ying, C., Wang, J., Li, J., Lai, X., & Wang, G. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106933 · Cited by 34

Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data

Shen, Z., Wu, Q., Wang, Z., Chen, G., & Lin, B. (2021) · Sensors · doi:10.3390/s21113663 · Cited by 34

Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0

Niño-Adan, I., Landa-Torres, I., Portillo, E., & Manjarrés, D. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104807 · Cited by 38

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Moukheiber, L., Mangione, W., Moukheiber, M., Maleki, S., Falls, Z., Gao, M., & Samudrala, R. (2022) · Molecules · doi:10.3390/molecules27093021 · Cited by 35

Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm

Osmani, A., Bagherzadeh, J., & Gharehchopogh, F. S. (2020) · The Computer Journal · doi:10.1093/comjnl/bxz163 · Cited by 33

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Naserzadeh, Z., & Nohegar, A. (2023) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2023.105092 · Cited by 33

Multiple-criteria decision making, feature selection, and deep learning: A golden triangle for heart disease identification

Najafi, A., Nemati, A., Ashrafzadeh, M., & Zolfani, S. H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106662 · Cited by 33

A Deep Reinforcement Learning Method Based on a Transformer Model for the Flexible Job Shop Scheduling Problem

Xu, S., Li, Y., & Li, Q. (2024) · Electronics · doi:10.3390/electronics13183696 · Cited by 37

Feature selection model based on clustering and ranking in pipeline for microarray data

Sahu, B., Dehuri, S., & Jagadev, A. K. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.07.004 · Cited by 33

A novel chaotic chicken swarm optimization algorithm for feature selection

Khaled, A., Hassanien, A. E., & Bhattacharyya, S. (2017) · International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) · doi:10.1109/icrcicn.2017.8234517 · Cited by 33

Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions

Sánchez, A., Soguero-Ruíz, C., Mora-Jiménez, I., Flores, F. J. R., Lehmann, D. J., & Rubio-Sánchez, M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.01.054 · Cited by 33

Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis

Jalalifar, S. A., Soliman, H., Sahgal, A., & Sadeghi-Naini, A. (2022) · Cancers · doi:10.3390/cancers14205133 · Cited by 15

Feature selection methods in machine learning: A review

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