Cited by 5,189Statistical Methods and InferenceText and Document Classification TechnologiesFace and Expression Recognition

The DOI matches its Crossref record; 84 of the 92 references it cites were found in a registry or an index.

A survey on feature selection methods

Chandrashekar, Girish, Sahin, Ferat·2014·Computers & Electrical Engineering

DOI 10.1016/j.compeleceng.2013.11.024

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.compeleceng.2013.11.024

Cites

84 of 84 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Particle swarm optimization
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,178
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Genetic Algorithms
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,818
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954

Show all 92 references

Research works printed but not linked: 5

Koller D, Sahami M. Towards optimal feature selection. In: ICML, vol. 96; 1996. p. 284–92.
Acuna, 2003, A comparison of feature selection procedures for classifier based on kernel density estimation, Proc Comput Commun Control Technol, 1, 468
Stearns S. On selecting features for pattern classifiers. In: Proceedings of the 3rd international conference on pattern recognition; 1976. p. 71–5.
Peng, 2009, Lazy learner text categorization algorithm based on embedded feature selection, J Syst Eng Electron, 20, 651
Dietterich, 1997, Machine learning research: four current directions, Artif Intell Mag, 18, 97

Sorted by citations

Items 301–400 of 1,004

A Genetic Programming approach for feature selection in highly dimensional skewed data
Viegas, F. A. R., da Rocha, L. C. D., Gonçalves, M. A., Mourão, F., Sá, G., Salles, T., Andrade, G., & Sandin, I. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.050 · Cited by 94
Ensemble of feature selection methods: A hesitant fuzzy sets approach
Ebrahimpour, M. K., & Eftekhari, M. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.11.021 · Cited by 93
TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Chkirbene, Z., Erbad, A., Hamila, R., Mohamed, A. M. S., Guizani, M., & Hamdi, M. (2020) · IEEE Access · doi:10.1109/access.2020.2994931 · Cited by 94
Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection
Ali, W., & Malebary, S. J. (2020) · IEEE Access · doi:10.1109/access.2020.3003569 · Cited by 93
A feature selection method via analysis of relevance, redundancy, and interaction
Wang, L., Jiang, S., & Jiang, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115365 · Cited by 92
Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization
Deng, S., Su, J., Zhu, Y., Yu, Y., & Xiao, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122502 · Cited by 96
An experimental comparison of feature selection methods on two-class biomedical datasets
Drotár, P., Gazda, J., & Smékal, Z. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.08.010 · Cited by 92
A feature selection approach to find optimal feature subsets for the network intrusion detection system
Kang, S., & Kim, K. J. (2016) · Cluster Computing · doi:10.1007/s10586-015-0527-8 · Cited by 92
Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewPreprint
Ghojogh, B., Samad, M. N., Mashhadi, S. A., Kapoor, T., Ali, W., Karray, F. O., & Crowley, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02845 · Cited by 139
Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition
Lü, L., Yan, J., & de Silva, C. W. (2015) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2015.01.037 · Cited by 91
Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection
Jia, H., Li, J., Song, W., Peng, X., Lang, C., & Li, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2919991 · Cited by 91
A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects
Battineni, G., Chintalapudi, N., Amenta, F., & Traini, E. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9072146 · Cited by 98
A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling
Elavarasan, D., Vincent, P. M. D. R., Srinivasan, K., & Chang, C. (2020) · Agriculture · doi:10.3390/agriculture10090400 · Cited by 97
Evaluation of feature selection methods based on artificial neural network weights
da Costa, N. L., de Lima, M. D., & Barbosa, R. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114312 · Cited by 91
A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
Mandal, M., Singh, P. K., Ijaz, M. F., Shafi, J., & Sarkar, R. (2021) · Sensors · doi:10.3390/s21165571 · Cited by 98
An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets
Mostafa, R. R., Khedr, A. M., Aghbari, Z. A., Afyouni, I., Kamel, I., & Ahmed, N. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111218 · Cited by 92
Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal
Cerrada, M., Sánchez, R., Cabrera, D. R., Zurita, G., & Li, C. (2015) · Sensors · doi:10.3390/s150923903 · Cited by 90
Feature selection using binary particle swarm optimization with time varying inertia weight strategies
Mafarja, M. M., Jarrar, R., Ahmad, S., & Abusnaina, A. (2018) · International Conference on Future Networks and Distributed Systems (ICFNDS) · doi:10.1145/3231053.3231071 · Cited by 93
A machine learning based approach to detect malicious android apps using discriminant system calls
Vinod, P., Zemmari, A., & Conti, M. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.11.021 · Cited by 90
Multi-Feature Fusion Approach for Epileptic Seizure Detection From EEG Signals
Radman, M., Moradi, M., Chaibakhsh, A., Kordestani, M., & Saif, M. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3026032 · Cited by 90
Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data
Piri, J., Mohapatra, P., Acharya, B., Gharehchopogh, F. S., Gerogiannis, V. C., Kanavos, A., & Manika, S. (2022) · Mathematics · doi:10.3390/math10152742 · Cited by 90
BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection
Khodadadi, N., Khodadadi, E., Al-Tashi, Q., El‐kenawy, E. M., Abualigah, L., Abdulkadir, S. J., Alqushaibi, A., & Mirjalili, S. (2023) · IEEE Access · doi:10.1109/access.2023.3310429 · Cited by 90
Machine Learning and Statistical Analysis for Materials Science: Stability and Transferability of Fingerprint Descriptors and Chemical Insights
Pankajakshan, P., Sanyal, S., de Noord, O. E., Bhattacharya, I., Bhattacharyya, A., & Waghmare, U. V. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b04229 · Cited by 90
A multi-centrality index for graph-based keyword extraction
Vega‐Oliveros, D. A., Gomes, P. S., Milios, E., & Berton, L. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102063 · Cited by 89
Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets
Ren, K., Fang, W., Qu, J., Zhang, X., & Shi, X. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124897 · Cited by 89
The era of big data: Genome-scale modelling meets machine learning
Antonakoudis, A., Barbosa, R., Kotidis, P., & Kontoravdi, C. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.011 · Cited by 92
Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms
Farsi, M., Mohamadian, N., Ghorbani, H., Wood, D. A., Davoodi, S., Moghadasi, J., & Alvar, M. A. (2021) · Natural Resources Research · doi:10.1007/s11053-021-09852-2 · Cited by 89
Modified crayfish optimization algorithm for solving multiple engineering application problems
Jia, H., Zhou, X., Zhang, J., Abualigah, L., Yıldız, A. R., & Hussien, A. G. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10738-x · Cited by 89
Review of machine learning approaches for predicting mechanical behavior of composite materials
Sharma, H., Arora, G., Singh, M. K., Ayyappan, V., Bhowmik, P., Rangappa, S. M., & Siengchin, S. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07616-8 · Cited by 112
“Input feature selection is the method of determining the most important variables, often referred to as features, that have the greatest impact on an ML model’s output [111].”
Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection
Ali, W. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080910 · Cited by 88
Tradeoffs in modeling performance of highly configurable software systems
Kolesnikov, S. S., Siegmund, N., Kästner, C., Grebhahn, A., & Apel, S. (2018) · Software & Systems Modeling · doi:10.1007/s10270-018-0662-9 · Cited by 88
An improved feature selection algorithm based on graph clustering and ant colony optimization
Ghimatgar, H., Kazemi, K., Helfroush, M. S., & Aarabi, A. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.025 · Cited by 88
A new semantic-based feature selection method for spam filtering
Méndez, J. R., Cotos-Yáñez, T. R., & Ruano-Ordás, D. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.12.008 · Cited by 89
Bad Smell Detection Using Machine Learning Techniques: A Systematic Literature Review
Al-Shaaby, A., Aljamaan, H., & Alshayeb, M. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-019-04311-w · Cited by 89
Augmented whale feature selection for IoT attacks: Structure, analysis and applications
Mafarja, M. M., Heidari, A. A., Habib, M., Faris, H., Thaher, T., & Aljarah, I. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.05.020 · Cited by 88
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data
Tang, S., Davarmanesh, P., Song, Y., Koutra, D., Sjoding, M. W., & Wiens, J. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa139 · Cited by 105
Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field
Sabah, M., Mehrad, M., Ashrafi, S. B., Wood, D. A., & Fathi, S. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.108125 · Cited by 88
Deep Learning Based Hybrid Intrusion Detection Systems to Protect Satellite Networks
Azar, A. T., Shehab, E., Mattar, A., Hameed, I. A., & Elsaid, S. A. (2023) · Journal of Network and Systems Management · doi:10.1007/s10922-023-09767-8 · Cited by 88
“In [16], authors proposed a new approach-based intrusion detection and compared it with different machine learning techniques, including SVM, AdaBoost, decision tree, and MLP, to classify normal and anomalous traffic.”
Method
An efficient machine learning approach to establish structure-property linkages
Jung, J., Yoon, J. I., Park, H. K., Kim, J. Y., & Kim, H. S. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.09.034 · Cited by 88
A utility-based news recommendation system
Zihayat, M., Ayanso, A., Zhao, X., Davoudi, H., & An, A. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.12.001 · Cited by 87
An ensemble feature selection method for high-dimensional data based on sort aggregation
Wang, J., Xu, J. M., Zhao, C., Peng, Y. P., & Wang, H. (2019) · Systems Science & Control Engineering · doi:10.1080/21642583.2019.1620658 · Cited by 87
Predicting lane-changing risk level based on vehicles’ space-series features: A pre-emptive learning approach
Chen, T., Shi, X., Wong, Y. D., & Yu, X. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102646 · Cited by 88
Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches
Ramesh, S., Sasikala, S., & Paramanandham, N. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10351-4 · Cited by 87
bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection
Shekhawat, S. S., SHARMA, H. K., Kumar, S., Nayyar, A., & Qureshi, B. (2021) · IEEE Access · doi:10.1109/access.2021.3049547 · Cited by 87
Simplifying approach to node classification in Graph Neural Networks
Maurya, S. K., Liu, X., & Murata, T. (2022) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101695 · Cited by 103
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Yan, X., Yue, T., Winkler, D. A., Yin, Y., Zhu, H., Jiang, G., & Yan, B. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00070 · Cited by 89
Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods
Tang, X., Li, S., Tan, M., & Shi, W. (2020) · Journal of Forecasting · doi:10.1002/for.2661 · Cited by 98
Bolt-looseness detection by a new percussion-based method using multifractal analysis and gradient boosting decision tree
Wang, F., & Song, G. (2020) · Structural Health Monitoring · doi:10.1177/1475921720912780 · Cited by 86
An evolutionary multi-objective optimization framework of discretization-based feature selection for classification
Zhou, Y., Kang, J., Kwong, S., Wang, X., & Zhang, Q. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100770 · Cited by 88
Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects
Eckardt, J., Bornhäuser, M., Wendt, K., & Middeke, J. M. (2020) · Blood Advances · doi:10.1182/bloodadvances.2020002997 · Cited by 86
Anomalous sound event detection: A survey of machine learning based methods and applications
Mnasri, Z., Rovetta, S., & Masulli, F. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11817-9 · Cited by 94
Chaotic binary Group Search Optimizer for feature selection
Abualigah, L., & Diabat, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116368 · Cited by 89
Novel Improved Salp Swarm Algorithm: An Application for Feature Selection
Zivkovic, M. Z., Stoean, C., Chhabra, A., Budimirović, N., Petrović, A., & Bačanin, N. (2022) · Sensors · doi:10.3390/s22051711 · Cited by 86
Hierarchical Harris hawks optimizer for feature selection
Peng, L., Cai, Z., Heidari, A. A., Zhang, L., & Chen, H. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.01.014 · Cited by 86
“Feature selection is mainly divided into the ﬁltering method [11], the embedding method [12], and the wrapping method [13].”
Method
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and ChallengesPreprint
Ashmore, R., Călinescu, R., & Paterson, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04223 · Cited by 86
A Survey of Data Semantization in Internet of Things
Shi, F., Li, Q., Zhu, T., & Ning, H. (2018) · Sensors · doi:10.3390/s18010313 · Cited by 85
Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction
Tsai, C., Sue, K.-L., Hu, Y., & Chiu, A. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.03.018 · Cited by 85
Utilizing XAI Technique to Improve Autoencoder based Model for Computer Network Anomaly Detection with Shapley Additive Explanation(SHAP)
Roshan, K., & Zafar, A. (2021) · International journal of Computer Networks & Communications · doi:10.5121/ijcnc.2021.13607 · Cited by 96
Similarity of feature selection methods: An empirical study across data intensive classification tasks
Dessì, N., & Pes, B. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.069 · Cited by 86
Dimension reduction methods for microarray data: a review
Aziz, R. M., Verma, C. K., & Srivastava, N. (2017) · AIMS bioengineering · doi:10.3934/bioeng.2017.1.179 · Cited by 87
Deep-FS: A feature selection algorithm for Deep Boltzmann Machines
Taherkhani, A., Cosma, G., & McGinnity, T. M. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.040 · Cited by 86
General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution
Al–Qerem, A., Kharbat, F. F., Nashwan, S. I., Ashraf, S., & blaou, K. (2020) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147720911009 · Cited by 84
Classification of alcoholic EEG signals using wavelet scattering transform-based features
Buriro, A. B., Ahmed, B., Baloch, G. L., Ahmed, J., Shoorangiz, R., Weddell, S. J., & Jones, R. D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104969 · Cited by 92
Machine learning in nutrient management: A review
Ennaji, O., Vergütz, L., & Allali, A. E. (2023) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2023.06.001 · Cited by 101
Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning
Strauss, P., Schmitz, M., Wöstmann, R., & Deuse, J. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622076 · Cited by 89
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., & Müller, K. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3020020 · Cited by 396
A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification
Jiménez-Cordero, A., Morales, J. M., & Pineda, S. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.12.009 · Cited by 87
Comparing different feature selection algorithms for cardiovascular disease prediction
Hasan, N., & Bao, Y. (2020) · Health and Technology · doi:10.1007/s12553-020-00499-2 · Cited by 91
Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods
Zhao, X., Kim, J., Warns, K., Wang, X., Ramuhalli, P., Çetiner, S. M., Kang, H. G., & Golay, M. W. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.696785 · Cited by 89
Feature selection revisited in the single-cell era
Yang, P., Huang, H. M., & Liu, C. (2021) · Genome biology · doi:10.1186/s13059-021-02544-3 · Cited by 87
Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems
Nguyen, K. T. P., Medjaher, K., & Gogu, C. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108383 · Cited by 90
Unsupervised feature selection via adaptive autoencoder with redundancy control
Gong, X., Yu, L. M., Wang, J., Zhang, K., Bai, X. P., & Pal, N. R. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.004 · Cited by 83
Impact of Chaos Functions on Modern Swarm Optimizers
Emary, E., & Zawbaa, H. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158738 · Cited by 82
Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification
Elyasigomari, V., Lee, D. A., Screen, H. R. C., & Shaheed, M. H. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.01.016 · Cited by 84
A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis
Sawhney, R., Mathur, P., & Shankar, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95162-1_30 · Cited by 82
Feature selection in single and ensemble learning‐based bankruptcy prediction models
Lin, W., Lu, Y., & Tsai, C. (2018) · Expert Systems · doi:10.1111/exsy.12335 · Cited by 82
Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm
Liao, X., Zhou, G., Zhang, Z., Lu, J., & Ma, J. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03906-9 · Cited by 83
A Jaya algorithm based wrapper method for optimal feature selection in supervised classification
Das, H., Naik, B., & Behera, H. S. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.05.002 · Cited by 82
Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions
Mahendran, N., Vincent, P. M. D. R., Srinivasan, K., & Chang, C. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.603808 · Cited by 82
Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network
Zhang, D., Chen, Y., Chen, Y., Ye, S., Cai, W., Jiang, J., Xu, Y., Zheng, G., & Chen, M. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6260022 · Cited by 82
Retracted
Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN
Kuang, X., Wang, F., Hernandez, K. M., Zhang, Z., & Grossman, R. L. (2022) · Scientific Reports · doi:10.1038/s41598-022-06449-4 · Cited by 82
Explainable artificial intelligence modeling to forecast bitcoin prices
Goodell, J. W., Jabeur, S. B., Saâdaoui, F., & Nasir, M. A. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102702 · Cited by 93
Interpretable Anomaly Prediction: Predicting anomalous behavior in industry 4.0 settings via regularized logistic regression tools
Langone, R., Cuzzocrea, A., & Skantzos, N. (2020) · Data & Knowledge Engineering · doi:10.1016/j.datak.2020.101850 · Cited by 81
Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine
Singh, M., & Shaik, A. G. (2018) · Measurement · doi:10.1016/j.measurement.2018.09.013 · Cited by 81
Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection
Haq, A. U., Zhang, D., Peng, H., & Rahman, S. U. (2019) · IEEE Access · doi:10.1109/access.2019.2947701 · Cited by 87
Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies
Moskowitz, C. S., Welch, M. L., Jacobs, M. A., Kurland, B. F., & Simpson, A. L. (2022) · Radiology · doi:10.1148/radiol.211597 · Cited by 83
OSFSMI: Online stream feature selection method based on mutual information
Rahmaninia, M., & Moradi, P. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.08.034 · Cited by 80
Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators
Peng, Y., Albuquerque, P. H. M., Kimura, H., & Saavedra, C. A. P. B. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100060 · Cited by 94
Efficient spam and phishing emails filtering based on deep learning
Magdy, S., Abouelseoud, Y., & Mikhail, M. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.108826 · Cited by 80
Particle guided metaheuristic algorithm for global optimization and feature selection problems
Kwakye, B. D., Li, Y., Mohamed, H. H., Baidoo, E., & Asenso, T. Q. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123362 · Cited by 80
Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection
Wang, L., Zhao, N., Chen, J., Li, P., Zhang, W., & Sui, K. (2020) · 2020 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws49710.2020.00026 · Cited by 84
Prediction of prices for used car by using regression models
Monburinon, N., Chertchom, P., Kaewkiriya, T., Rungpheung, S., Buya, S., & Boonpou, P. (2018) · International Conference on Business and Industrial Research (ICBIR) · doi:10.1109/icbir.2018.8391177 · Cited by 79
Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques
Al-Shabandar, R., Hussain, A. J., Liatsis, P., & Keight, R. (2019) · IEEE Access · doi:10.1109/access.2019.2943351 · Cited by 79
Half-Quadratic Minimization for Unsupervised Feature Selection on Incomplete Data
Shen, H. T., Zhu, Y., Zheng, W., & Zhu, X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3009632 · Cited by 79
Feature Selection in Machine Learning: Methods and Comparison
Kaur, A., Guleria, K., & Trivedi, N. K. (2021) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite51222.2021.9404623 · Cited by 79
Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods
Saberi-Movahed, F., Mohammadifard, M., Mehrpooya, A., Rezaei-Ravari, M., Berahmand, K., Rostami, M., Karami, S., Najafzadeh, M., Hajinezhad, D., Jamshidi, M., et al. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105426 · Cited by 80
Different approaches for identifying important concepts in probabilistic biomedical text summarization
Moradi, M., & Ghadiri, N. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.004 · Cited by 82
Automated classification of building information modeling (BIM) case studies by BIM use based on natural language processing (NLP) and unsupervised learning
Jung, N., & Lee, G. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.04.007 · Cited by 84
The optimal combination of feature selection and data discretization: An empirical study
Tsai, C., & Chen, Y.-C. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.091 · Cited by 78
Monitoring sustainable development by means of earth observation data and machine learning: a review
Ferreira, B., Iten, M. D. C., & Silva, R. (2020) · Environmental Sciences Europe · doi:10.1186/s12302-020-00397-4 · Cited by 82

Authors: Chandrashekar, G., & Sahin, F.
Affiliations: Rochester Institute of Technology (US)
Corresponding author: Chandrashekar, Girish
ORCID: Sahin, Ferat
Venue: Computers & Electrical Engineering
Publisher: Elsevier BV
Published: 2014-01
Volume: 40
Issue: 1
Pages: 16-28
ISSN: 0045-7906 (ISSN-L)
DOI: 10.1016/j.compeleceng.2013.11.024
OpenAlex: W2167101736
Cited by: 5,189
References: 92
Citations by year: 2026: 351 · 2025: 517 · 2024: 575 · 2023: 652 · 2022: 655 · 2021: 651 · 2020: 498 · 2019: 440 · 2018: 378 · 2017: 239 · 2016: 138 · 2015: 73 · 2014: 21
FWCI: 68.25
Type: journal article
Language: en
Open access: closed
Locations: Computers & Electrical Engineering (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords: feature selection, high-dimensional data, filter methods, wrapper methods, embedded methods, dimensionality reduction, machine learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A survey on feature selection methods

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks