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Theoretical and Empirical Analysis of ReliefF and RReliefF
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Hoq, M., Uddin, M. N., & Park, S.-B. (2021) · Diagnostics · doi:10.3390/diagnostics11061076 · Cited by 93
“However, it was empirically and theoretically proved that the performance of core relief-based algorithms (RBA) decreases drastically as the number of irrelevant features becomes enormous [26].”
BackgroundMutual information criterion for feature selection from incomplete data
Qian, W., & Shu, W. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.105 · Cited by 83
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Samà, A., Pérez‐López, C., Romagosa, J., Rodríguez-Martín, D., Catalá, A., Cabestany, J., Pérez-Martínez, D. A., & Rodríguez‐Molinero, A. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346150 · Cited by 83
Online Learning from Trapezoidal Data Streams
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Human Activity Recognition in AAL Environments Using Random Projections
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“Extensions of RELIEF to handle multiple classes have been proposed (Kononenko, 1994; Robnik-Sikonja and Kononenko, 2003; Sun and Li, 2006).”
Yang, Z., Shao, Y. F., Li, K., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.04.016 · Cited by 82
Feature Selection With $\ell_{2,1-2}$ Regularization
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Pinacho-Castellanos, S. A., García‐Jacas, C. R., Gilson, M. K., & Brizuela, C. A. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00251 · Cited by 85
Feature selection with redundancy-complementariness dispersion
Chen, Z., Wu, C., Zhang, Y., et al. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.07.004 · Cited by 81
Machine learning methods to forecast temperature in buildings
Mateo, F., Carrasco, J. J., Sellami, A., Millán-Giraldo, M., Domínguez, M., & Soria‐Olivas, E. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.030 · Cited by 80
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“For the strategy of high-dimensional space, ICA is applied to the entire data set, and then, the most informative features are selected by using the Relief method [43].”
MethodAutomated data-driven modeling of building energy systems via machine learning algorithms
Rätz, M., Javadi, A. P., Baranski, M., Finkbeiner, K., & Müller, D. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109384 · Cited by 81
Bioinformatic-driven search for metabolic biomarkers in disease
Baumgärtner, C., Osl, M., Netzer, M., & Baumgartner, D. (2011) · Journal of Clinical Bioinformatics · doi:10.1186/2043-9113-1-2 · Cited by 83
“ReliefF (RF) - multivariate filter method - RF score relies on the concept that values of a significant feature are correlated with the feature values of an instance of the same class, and uncorrelated with the feature values of an instance of the other class - appropriate for multiple class problems - RF scores permi…”
MethodTowards a Memetic Feature Selection Paradigm [Application Notes
Zhu, Z., Jia, S., & Ji, Z. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.936311 · Cited by 79
“In this method, all features are ranked individually based on ReliefF [ 47 ].”
MethodDesign of input vector for day-ahead price forecasting of electricity markets
Amjady, N., & Daraeepour, A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.04.059 · Cited by 80
Chaudhuri, A., & Routray, A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2890332 · Cited by 78
“Then, using the difference between R–H and R–M, it creates an overall ranking, and corresponding weights for each of the attributes, which has been theoretically and empirically shown to be quite competent for feature selection [63].”
MethodRicci, M., Lazzaro, G. D., Pisani, A., Mercuri, N. B., Giannini, F., & Saggio, G. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2903627 · Cited by 79
“The feature selection was obtained combining ReliefF ranking [46] and Kruskal– Wallis feature-selection algorithms [47].”
MethodMassé, F., Gonzenbach, R. R., Arami, A., Paraschiv-Ionescu, A., Luft, A. R., & Aminian, K. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-015-0060-2 · Cited by 77
“To avoid over-fitting, the feature set was reduced using ReliefF algorithm [35] to K features to form the minimal feature set Ωepochs = {Nepoch × K features}.”
MethodDistributed ReliefF-based feature selection in Spark
Palma-Mendoza, R.-J., Rodriguez, D., & de‐Marcos, L. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-017-1145-y · Cited by 78
“The ReliefF algorithm was first described by Kononenko [17] and since then, many applications, extensions and improvements have been proposed.”
BackgroundMethodInfluentialPaulus, S., & Mahlein, A. (2020) · GigaScience · doi:10.1093/gigascience/giaa090 · Cited by 80
“Common techniques are principal component analysis [55], feature selection using recursive feature elimination [56], ReliefF [57], or correlation-based feature selection [58].”
Roy, S., Whitehead, T., Li, S.-Q., Ademuyiwa, F., Wahl, R., Dehdashti, F., & Shoghi, K. (2021) · bioRxiv · doi:10.1007/s00259-021-05489-8 · Cited by 67
Self-representation based dual-graph regularized feature selection clustering
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Recognizing Induced Emotions of Movie Audiences from Multimodal Information
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“To reduce the number of features, we used the ReliefF algorithm [60] and ranked the discriminative power of features for emotion recognition by means of performing regression with 20 nearest neighbours.”
MethodRise of Raman spectroscopy in neurosurgery: a review
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A novel filter feature selection algorithm based on relief
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Awadallah, M. A., Al‐Betar, M. A., Braik, M. S., Hammouri, A. I., Doush, I. A., & Zitar, R. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105675 · Cited by 76
Massively parallel feature selection: an approach based on variance preservation
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Local discriminative based sparse subspace learning for feature selection
Shang, R., Yang, M., Wang, W., Shang, F., & Jiao, L. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.026 · Cited by 75
Ensemble machine learning approach for electronic nose signal processing
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Nir, T. M., Villalón‐Reina, J. E., Prasad, G., Jahanshad, N., Joshi, S. H., Toga, A. W., Bernstein, M. A., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.05.037 · Cited by 79
“…used features derived from full DTI maps and TBSS skeletons using methods like Pearson correlation and ReliefF (Pearson et al., 1985; Robnik-(cid:2) Sikonja and Kononenko, 2003) to reduce the number of features (Graña et al., 2011; Haller at al., 2010; O ’ Dwyer et al., 2012), yielding accuracies…”
Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier
Vakharia, V., Gupta, V. K., & Kankar, P. K. (2017) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-017-0717-9 · Cited by 75
Chen, L., Zhang, A., & Xiao-guang, L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.02.005 · Cited by 78
On some aspects of minimum redundancy maximum relevance feature selection
Bugata, P., & Drotár, P. (2019) · Science China Information Sciences · doi:10.1007/s11432-019-2633-y · Cited by 74
Barraza, J. E. F., Droguett, E. L., & Martins, M. R. (2021) · Sensors · doi:10.3390/s21175888 · Cited by 75
“• Mutual Information • ReliefF [84] and RReliefF [85] • Random Forest • “AFS: An Attention-based mechanism for Supervised Feature Selection” [58] (AFS)”
MethodInfluentialObservational data-driven modeling and optimization of manufacturing processes
Sadati, N., Chinnam, R. B., & Nezhad, M. Z. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.10.028 · Cited by 74
“For example, filter meth-ods rank features based on specific criteria such as Fisher score (Duda et al., 2012) and mutual information (MI) (Kira & Rendell, 1992; Robnik-ˇSikonja & Kononenko, 2003).”
Hoang, N., Bui, D. T., & Liao, K. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.04.031 · Cited by 73
High dimensional very short-term solar power forecasting based on a data-driven heuristic method
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RETRACTED ARTICLE: Feature selection techniques in the context of big data: taxonomy and analysis
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“Minimum Redundancy, Maximum Relevance (mRmR) Selecting the feature based on relevance and redundancy.”
RetractedHashemi, A., Joodaki, M., Joodaki, N. Z., et al. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109046 · Cited by 73
Fuzzy criteria for feature selection
Vieira, S. M., Sousa, J. M. C., & Kaymak, U. (2011) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2011.09.009 · Cited by 73
‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification
Schuller, B. W., Hage, C., Schuller, D., & Rigoll, G. (2010) · Journal of New Music Research · doi:10.1080/09298210903430475 · Cited by 71
Deep learning based short term load forecasting with hybrid feature selection
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Gene–gene interactions contribute to eye colour variation in humans
Pośpiech, E., Draus‐Barini, J., Kupiec, T., Wojas‐Pelc, A., & Branicki, W. (2011) · Journal of Human Genetics · doi:10.1038/jhg.2011.38 · Cited by 71
Automatic evaluation of traumatic brain injury based on terahertz imaging with machine learning
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Classification of three anesthesia stages based on near-infrared spectroscopy signals
Liu, Z., Si, L., Shi, S., Li, J., Zhu, J., Lee, W. H., Lo, S.-L., Yan, X., Chen, B., Fu, F., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3409163 · Cited by 71
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Efficient feature selection techniques for sentiment analysis
Madasu, A., & Elango, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08409-z · Cited by 83
“Relief [4], Chi-Square [5] and Information Gain are some of the filter methods.”
MethodSouza, V. M. A., Giusti, R., & Batista, A. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.10.008 · Cited by 70
A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study
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“We selected 3 different metrices: LR beta value, relief algorithm [58], and information gain (InfGain) [59].”
MethodDevelopment of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification
Meng, R., Lv, Z., Yan, J., et al. (2020) · Remote Sensing · doi:10.3390/rs12193233 · Cited by 70
“The RELIEF-F algorithm can estimate discriminative power of certain features in multiclass classification problems with incomplete and noisy data in terms of their performances in separating samples of different classes close to each other [23,30].”
MethodGate-ID: WiFi-Based Human Identification Irrespective of Walking Directions in Smart Home
Zhang, J., Wei, B. W., Wu, F., et al. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3040782 · Cited by 70
“Various types of calculated features may not be equally useful, therefore, we first adopt ReliefF, a feature weighting method to estimate the importance weights of each type of features.”
BackgroundMethodInfluentialEnsemble Approach for Detection of Depression Using EEG Features
Avots, E., Jermakovs, K., Bachmann, M., Päeske, L., Ozcinar, C., & Anbarjafari, G. (2022) · Entropy · doi:10.3390/e24020211 · Cited by 70
“The algorithm penalizes the predictors that give different values to neighbors of the same class, and rewards predictors that give different values to neighbors of different classes [27].”
BackgroundAttribute Weighting via Differential Evolution Algorithm for Attribute Weighted Naive Bayes (WNB)
Wu, J. X., & Cai, Z. (2011) · UTS ePRESS (University of Technology Sydney) · Cited by 70
Dangerous driving behavior detection using video-extracted vehicle trajectory histograms
Chen, Z., Wu, C., Huang, Z., Lyu, N., Hu, Z., Zhong, M., Yang, C., & Ran, B. (2017) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2017.1305271 · Cited by 71
Nishad, A., Upadhyay, A. B., Pachori, R. B., & Acharya, U. R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.005 · Cited by 70
Constrained Laplacian Score for Semi-supervised Feature Selection
Benabdeslem, K., & Hindawi, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23780-5_23 · Cited by 70
“The spectral graph theory [11] represents a solid theoretical framework which has been the basis of many powerful existing feature selection methods such as ReliefF [ 21 ], Laplacian [20] , sSelect [10], SPEC[22] and Constraint score [17].”
BackgroundMethodWang, S., Hu, Y. S., Shen, Y., & Li, H. (2017) · International Journal of Neural Systems · doi:10.1142/s0129065717500368 · Cited by 69
Ebrahimy, H., Feizizadeh, B., Salmani, S., et al. (2020) · Environmental Earth Sciences · doi:10.1007/s12665-020-08953-0 · Cited by 69
Marjanović, M., Kralj, M., Supek, F., et al. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm061162u · Cited by 69
Feature Extraction for Next-Term Prediction of Poor Student Performance
Polyzou, A., & Karypis, G. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2913358 · Cited by 74
“, mutual information [24], relief [25].”
Sugan, N., Srinivas, N. S. S., Kumar, L. S., Nath, M. K., & Kanhe, A. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102763 · Cited by 72
A Feature Subset Selection Algorithm Automatic Recommendation Method
Wang, G., Song, Q., Sun, H., et al. (2013) · Cited by 69
Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique
Xu, Y. H., Dong, Z. Y., Guan, L., Zhang, R., Wong, K. P., & Luo, F. (2012) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2183898 · Cited by 68
“, the class of the instance is continuous, rather than judging whether two instances belong to the same class or not (absolute difference), the probability of difference is used [24].”
BackgroundMethodTri-modal Person Re-identification with RGB, Depth and Thermal Features
Møgelmose, A., Bahnsen, C., Moeslund, T. B., et al. (2013) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.52 · Cited by 68
“Moreover, individual feature weights w assign a weight to each particular depth feature value, pre-computed based on a training stage applying ReliefF [16].”
MethodAn efficient semi-supervised representatives feature selection algorithm based on information theory
Wang, Y., Wang, J., Liao, H., & Chen, H. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.011 · Cited by 68
He, Q., Li, X., Kim, D. W. N., Jia, X., Gu, X., Zhen, X., & Zhou, L. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.09.001 · Cited by 67
Chepurko, N., Marcus, R., Zgraggen, E., Fernandez, R. C., Kraska, T., & Karger, D. R. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3397230.3397235 · Cited by 67
“One popular embedded feature selection algorithm is Relief, which is considered to be effective and efficient in practice [39,57,63].”
Satapathy, S. K., Bhoi, A. K., Loganathan, D., Khandelwal, B., & Barsocchi, P. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102898 · Cited by 67
Automated Music Emotion Recognition: A Systematic Evaluation
Huq, A., Bello, J. P., & Rowe, R. C. (2010) · Journal of New Music Research · doi:10.1080/09298215.2010.513733 · Cited by 67
Learning the Spherical Harmonic Features for 3-D Face Recognition
Liu, Y., Wang, Y., Huang, D., et al. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2222897 · Cited by 67
“Finally, 3D FR experiments using selected SHF are performed and the results compared with the state of the art.”
A Hybrid Feature Selection Method for Complex Diseases SNPs
Alzubi, R., Ramzan, N., Alzoubi, H., & Amira, A. (2017) · IEEE Access · doi:10.1109/access.2017.2778268 · Cited by 67
“The proposed method results are compared with the Acc and F achieved using the Minimum Redundancy Maximum Relevance (mRMR) algorithm [35], ReliefF [14], Fast Correlation Based Feature Selection (FCBF) [36] and CMIM.”
MethodKukolja, D., Popović, S., Horvat, M., Kovač, B., & Ćosić, K. (2014) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2014.05.006 · Cited by 66
Ren, Q., Li, M., Li, H., Song, L., Si, W., & Liu, H. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12654 · Cited by 66
“The parameters of the RReliefF algorithm also have little influence on its weighted results (Robnik-Šikonja & Kononenko, 2003).”
BackgroundMethodInfluentialAutomated accurate schizophrenia detection system using Collatz pattern technique with EEG signals
Baygin, M., Yaman, O., Tuncer, T., et al. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102936 · Cited by 66
Enhanced NSGA-II-based feature selection method for high-dimensional classification
Li, M., Ma, H., Lv, S., Wang, L., & Deng, S. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120269 · Cited by 67
FREL: A Stable Feature Selection Algorithm
Li, Y., Si, J., Zhou, G., et al. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2341627 · Cited by 66
“The algorithms include Relief, ReliefF [31]–[33], Fisher score [34], En-Relief [4], En-ReliefF, En-Fisher, and variance reduction (VR)-Lmba.”
MethodAn integrated feature selection and cluster analysis techniques for case-based reasoning
Zhu, G., Hu, J., Qi, J., et al. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.11.006 · Cited by 66
“To evaluate the proposed hybrid CBR (HCBR), traditional CBR (TCBR) (Kolodner, 1993) and CBR combined with ReliefF (CBR-ReliefF) (Robnik-Šikonja and Kononenko, 2003), are conducted in the experiment for comparison.”
MethodInfluentialCervical Cancer Diagnosis based on Random Forest
Sun, G. (2017) · International Journal of Performability Engineering · doi:10.23940/ijpe.17.04.p12.446457 · Cited by 66
3-D face recognition: features, databases, algorithms and challenges
Patil, H. Y., Kothari, A., & Bhurchandi, K. M. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9431-0 · Cited by 68
Pan, C., Liu, J., Tang, J., Chen, X., Fang, C., Wu, Y., Geng, Y., Xu, C., Zhang, X., Wu, Z., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.07.011 · Cited by 65
Computational prediction of the localization of microRNAs within their pre-miRNA
Leclercq, M., Diallo, A. B., & Blanchette, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt466 · Cited by 66
Lertampaiporn, S., Thammarongtham, C., Nukoolkit, C., Kaewkamnerdpong, B., & Ruengjitchatchawalya, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks878 · Cited by 65
A PSO based hybrid feature selection algorithm for high-dimensional classification
Tran, B., Zhang, M., & Xue, B. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7744271 · Cited by 65
“In these methods, features are individually ranked based on their degrees of relevance using some measures such as distance [2], feature dispersion [3], or classification accuracy [4].”
MethodMachine learning-based multi-documents sentiment-oriented summarization using linguistic treatment
Abdi, A., Shamsuddin, S. M., Hasan, S., & Piran, M. J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.05.010 · Cited by 65
Validation of a Portable Device for Mapping Motor and Gait Disturbances in Parkinson’s Disease
Rodríguez-Molinero, A., Samà, A., Pérez-Martínez, D. A., Pérez López, C., Romagosa, J., Bayés, À., Sanz, P., Calopa, M., Gálvez-Barrón, C., de Mingo, E., et al. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3321 · Cited by 64
Wang, Y., & Makedon, F. S. (2004) · doi:10.1109/csb.2004.35 · Cited by 64
Xu, Z., Xuan, J., Liu, J., et al. (2016) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2016.34 · Cited by 64
“an extension of the Relief method that can solve the multiclass learning problem [43].”
MethodMonge-Álvarez, J., Hoyos-Barceló, C., San‐José‐Revuelta, L. M., & Casaseca‐de‐la‐Higuera, P. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2888998 · Cited by 64
“3) The Relieff algorithm [32] – a widely used supervised feature selection algorithm for two-class problems – was applied to the selected observations in order to identify the best 29 features in each part.”
MethodRobust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization
Deng, Z., Chung, F.-L., & Wang, S. (2010) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2010.2047947 · Cited by 64
“Kononenko [4] extended it to the multiclass case and accordingly presented an algorithm called ReliefF, which was further studied in-depth in [5].”
MethodA feature set measure based on Relief
Araúzo-Azofra, A., & Castro, J. L. (2004) · Cited by 63
Feature ranking for multi-target regression
Petković, M., Kocev, D., & Džeroski, S. (2019) · Machine Learning · doi:10.1007/s10994-019-05829-8 · Cited by 64
Subaşı, A., Doğan, Ş., & Tuncer, T. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03324-4 · Cited by 63
Shuzan, M. N. I., Chowdhury, M. H., Hossain, M. S., Chowdhury, M. E. H., Reaz, M. B. I., Uddin, M. M., Khandakar, A. A., Mahbub, Z. B., & Ali, S. H. M. (2021) · IEEE Access · doi:10.1109/access.2021.3095380 · Cited by 63
“RFS works much better to approximate the significance of the function for distance-based supervised models that use pairwise distances between the observations to predict [52], [53].”
BackgroundPersonal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition
Chen, H., Sun, S., Li, J., Yu, R., Li, N., Li, X., & Hu, B. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3137857 · Cited by 63
“Under the combination of ReliefF+SVM-poly1, the classification accuracy of IAD-PZ reached 88 (cid”
MethodInfluentialMulti-strategy ensemble binary hunger games search for feature selection
Ma, B. J., Liu, S., & Heidari, A. A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108787 · Cited by 63
Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection
Yang, J.-Q., Yang, Q.-T., Du, K.-J., Chen, C., Wang, H., Jeon, S.-W., Zhang, J., & Zhan, Z. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3232761 · Cited by 64
“Chen et al. [26] introduced the evolutionary multitasking framework into PSO, which identified a promising feature subset with high Relief-F values and generated two related tasks on the promising feature subset and the whole feature set, respectively.”
MethodLiu, J., Li, D., Shan, W., et al. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111018 · Cited by 63
Metadata
- Authors
- Robnik-Šikonja, M., & Kononenko, I.
- Affiliations
- University of Ljubljana (SI)
- ORCID
- Kononenko, Igor
- Venue
- Machine Learning
- Published
- 2003-10
- Volume
- 53
- Issue
- 1-2
- Pages
- 23-69
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1500895378
- Semantic Scholar
- d398ad942320c1c2cf64da23ba519870f9f1d67d
- Cited by
- 3,025
- References
- 45
- Citations by year
- 2026: 125 · 2025: 186 · 2024: 192 · 2023: 209 · 2022: 223 · 2021: 246 · 2020: 237 · 2019: 212 · 2018: 216 · 2017: 150 · 2016: 158 · 2015: 166 · 2014: 143 · 2013: 91 · 2012: 105
- FWCI
- 44.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
- Keywords
- Relief algorithm, RReliefF, feature selection, feature weighting, decision tree splitting, inductive logic programming, noise robustness, scalability, data sampling
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