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Theoretical and Empirical Analysis of ReliefF and RReliefF
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Dou, J., Song, Y., Wei, G., & Zhang, Y. (2021) · Information Sciences · doi:10.1016/j.ins.2021.10.057 · Cited by 30
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“FS methods are broadly classified into three groups: Wrap-per methods [5], [6], Embedded methods [5] and Filter methods [7], [8].”
MethodZhang, Y., Liu, J., Li, W., & Liang, S. (2023) · Remote Sensing · doi:10.3390/rs15041096 · Cited by 30
Detecting Abnormal Events via Hierarchical Dirichlet Processes
Zhang, X.-X., Liu, H., Gao, Y., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01307-2_27 · Cited by 30
“At the same time, Relief algorithm [15] and its variations [16] are widely accepted in feature selection application, However, due to the fact that the majority of our training data always come from only one class, so neither can we apply Relief algorithm directly.”
BackgroundMethodQuality assessment of individual classifications in machine learning and data mining
Kukar, M. (2005) · Knowledge and Information Systems · doi:10.1007/s10115-005-0203-z · Cited by 30
“[13], two kinds of Assistant (ID3-like decision trees) with both information gain and ReliefF [20] impurity measures.”
BackgroundInfluentialExploring epistasis in candidate genes for antisocial personality disorder
Arias, J. M. C., Acosta, C. A. P., Valencia, J. G., Montoya, G., Viana, J. C. A., Campo, O., Flórez, A. F., Medellin, B. E. C., Montoya, W. R., López‐Jaramillo, C., et al. (2011) · Psychiatric Genetics · doi:10.1097/ypg.0b013e3283437175 · Cited by 32
“…and successful attribute estimators are especially good in detecting conditional dependencies; they are efficient, aware of the contextual information, and can correctly estimate the quality of attributes in problems with strong dependencies between attributes (Robnik-S̆ikonja and Kononenko, 2003).”
BackgroundLarge-margin feature selection for monotonic classification
Hu, Q., Pan, W., Song, Y., et al. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.01.011 · Cited by 30
Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease
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“It is proportional to the difference between two conditional probabilities, namely, the probability of the attribute ’ s value being different conditioned on the given nearest miss and nearest hit respectively [5].”
BackgroundFall recognition using wearable technologies and machine learning algorithms
Harris, A., True, H., Hu, Z., Cho, J. Y., Fell, N., & Sartipi, M. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7841080 · Cited by 30
“, SelectKBest, ReliefF, and Robust feature selections [23], [24], [25].”
Discovering Support and Affiliated Features from Very High DimensionsPreprint
Zhai, Y., Tan, M., Tsang, I. W.-H., et al. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6477 · Cited by 30
“For fair comparisons, all methods except ReliefF, which is integrated in MATLAB R2011b, are implemented in C++ with MATLAB interface.”
MethodInfluentialSaadatmand, H., & Akbarzadeh-T, M.-R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3416196 · Cited by 29
“These include six SOTA filter and embedded methods, Inf-FS-S [8], Inf-FS-U [8], MRMR [9], ReliefF [62], Correlation-based FS (CFS) [63], Random Forest (RF) [57], and two imbalanced approaches, PSOFS-FC [26] and SYMON [27], along with four single-objective methods: PPSO [11], HFS-C-P [20], SIFE [36],…”
MethodAttallah, O., Ibrahim, R. A., & Zakzouk, N. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-96290-2 · Cited by 36
“On the other hand, ReliefF 55 is a methodology for feature selection that determines pertinent attributes by assessing their significance in distinguishing between instances of identical and disparate classes.”
Exploring autoencoders for unsupervised feature selection
Chandra, B., & Sharma, R. K. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280391 · Cited by 30
“Feature selection methods can be categorized as supervised [1], [2], semi supervised [3], [4] or unsupervised [5] based on whether the class information is available or not.”
BackgroundReliefMSS: a variation on a feature ranking ReliefF algorithm
Chıkhı, S., & Benhammada, S. (2009) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2009.029085 · Cited by 29
“We use the following measures defined in Robnik-Sikonja and Kononenko (2003a): Separability is the difference between the lowest estimate of the important attributes and the highest estimate of the unimportant attributes. s = WIworst – WRbest.”
MethodBeck, D., & Foster, J. A. (2015) · BioData Mining · doi:10.1186/s13040-015-0055-3 · Cited by 31
“In addition to the RF and LR classifiers, we also calculated reliefF rankings and correlations between the features and BV. ReliefF is a feature selection algorithm that estimates the relevance of each feature by how well it separates similar samples into classes [16].”
MethodBertin, G. I., Sabbagh, A., Argy, N., Salnot, V., Ezinmègnon, S., Agbota, G., Ladipo, Y., Alao, J. M., Sagbo, G., Guillonneau, F., et al. (2016) · Scientific Reports · doi:10.1038/srep26773 · Cited by 29
Forecasting of Wind Speed using Feature Selection and Neural Networks
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Wang, Y., Zhang, H., Qiu, C., et al. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/5078268 · Cited by 29
“Four methods, ELM PSO [15], ELM FS [29], SVM FODPSO [10], and RReliefF [30], are used for comparison.”
MethodHoang, N., Liao, K., & Tran, X.-L. (2018) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-018-0287-2 · Cited by 29
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New filter approaches for feature selection using differential evolution and fuzzy rough set theory
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Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
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“Its detail process can be found in [36].”
BackgroundScalable feature selection using ReliefF aided by locality‐sensitive hashing
Eiras‐Franco, C., Guijarro‐Berdiñas, B., Alonso‐Betanzos, A., & Bahamonde, A. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22546 · Cited by 29
“15 While ReliefF has been thoroughly tested 16,17 and has been shown to be useful in a great variety of problems, 18 – 23 its great computational complexity makes its use on large ‐ scale data sets prohibitively costly.”
BackgroundSchnur, C., Goodarzi, P., Lugovtsova, Y., Bulling, J., Prager, J., Tschöke, K., Moll, J., Schütze, A., & Schneider, T. (2022) · Sensors · doi:10.3390/s22010406 · Cited by 31
“Feature Extraction Methods Adaptive linear approximation ALA [18] Principal component analysis PCA [19] Best Fourier coefficients BFC [20] Best Daubechies wavelets BDW [21] Statistical moments SM [22] Feature Selection Methods Recursive feature elimination support vector machines * RFE-SVM [23,24] RELIEFF * RELIEFF [2…”
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A Hybrid GCN and Filter‐Based Framework for Channel and Feature Selection: An fNIRS‐BCI Study
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Exploration and Reduction of the Feature Space by Hierarchical Clustering
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“On the other side, as we will see, our solution does not require any special nor complex parameter tuning process that some other competitive approaches require (see for instance, ReliefF [18]).”
BackgroundMethodInfluentialDavis, N. A., Lareau, C. A., White, B. C., Pandey, A., Wiley, G. B., Montgomery, C. G., Gaffney, P. M., & McKinney, B. A. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21739 · Cited by 29
“These three methods, EC, RJ, and Relief-F, each have different advantages discussed below.”
InfluentialHybrid system for lymphatic diseases diagnosis
Elshazly, H. I., Azar, A. T., ElKorany, A., et al. (2013) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2013.6637195 · Cited by 29
Network intrusion detection using feature selection and Decision tree classifier
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“In this paper, we have considered three different approaches for feature selection: Chi square [10], Information Gain [25] and ReliefF [ 19 ] which is based on filter approach.”
MethodEnsemble Classification Based on Feature Selection for Environmental Sound Recognition
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García‐Jacas, C. R., Marrero‐Ponce, Y., Cortés‐Guzmán, F., Suárez‐Lezcano, J., Martínez-Rios, F., García‐González, L. A., Pupo-Meriño, M., & Martínez‐Mayorga, K. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00011 · Cited by 30
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“Robnik-Šikonja M and Kononenko I, Theoretical and Empirical Analysis of ReliefF and RReliefF.”
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“PhyloRelief is an algorithm that introduces the Relief [21,22] strategy of feature weighting in a phylogenetic context to identify those OTUs or groups of OTUs that are responsible for the differentiation between classes of samples (i.”
MethodFeature selection strategies for classifying high dimensional astronomical data sets
Donalek, C., Djorgovski, S. G., Mahabal, A. A., et al. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691731 · Cited by 28
Automatic assessment of communication skill in interview-based interactions
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“To understand the significance of attributes and features, we used reliefF ranking algorithm [34].”
MethodRueangsirarak, W., Zhang, J., Aslam, N., Ho, E. S. L., & Shum, H. P. H. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2880871 · Cited by 28
“Available: https://doi.org/10.1007/978-3-540-35488-8_13 [23] M. Robnik-Šikonja and I. Kononenko, “Theoretical and empirical analysis of ReliefF and RReliefF,” Mach.”
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“310 2) Feature-Ranking Techniques: The employed rank-based 311 feature selection techniques were: 1) Correlation-based Fea312 ture Selection (CFS) [48], 2) Infinite Latent Feature Selec313 tion, (ILFS) [49], 3) Least Absolute Shrinkage and Selection 314 Operator (Lasso) [50], 4) Local Learning-based Clustering 315 Fea…”
MethodInfluentialAnsarinasab, S., Parastesh, F., Ghassemi, F., Rajagopal, K., Jafari, S., & Ghosh, D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106461 · Cited by 28
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“Theoretical justification and recent advances of Re1ief-F can also be found in [ 20 ].”
BackgroundImproving PLS–RFE based gene selection for microarray data classification
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“In this study, RReliefF, a feature weighting approach considering interrelationship among features, is utilized for feature selection in both classification levels for its effectiveness [9].”
MethodInfluentialDash, R. (2017) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2017.08.005 · Cited by 28
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“Previous studies [4,5] have shown that KNN classification induces better accuracy than other classification methods on the same dataset.”
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Kanaris, I., & Stamatatos, E. (2007) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2007.107 · Cited by 28
Online random forests based on CorrFS and CorrBE
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“Furthermore, the framework allows a fair comparison between other batch mode feature evaluation approaches; Gini index [3], ReliefF [8, 11, 12], and gain ratio [10].”
MethodChen, X., Fang, T., Huo, H., et al. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2411331 · Cited by 27
“ReliefF evaluates the relevance of features by feature weighting according to how well features distinguish samples [23].”
BackgroundMethodInfluentialLarge Margin Subspace Learning for feature selection
Liu, B., Fang, B., Liu, X., Chen, J., Huang, Z., & He, X. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.02.012 · Cited by 27
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Neglecting spatial autocorrelation causes underestimation of the error of sugarcane yield models
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de Moura, J., Novo, J., & Ortega, M. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852196 · Cited by 28
“This strategy, one of the most popular ones, estimates the quality of the features according to how well their values distinguish between the instances that are near each other, thus appreciating its synergistic effect [28].”
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“ReliefF [34,35] can detect conditional dependencies between features, and can evaluate those features on the basis of how well they distinguish between similar instances.”
BackgroundPavement Distress Recognition via Wavelet-Based Clustering of Smartphone Accelerometer Data
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“51 RELIEF is a feature-weighting algorithm, where each feature is assigned a weight depending on how ‘‘useful’’ it is in the context of predicting the response.”
BackgroundUzun, B., & Ballı, S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07402-2 · Cited by 27
“The use of both the mean and variance of the difference and distance between data samples as a criterion for determining the feature weight vector improves the stability of the outcome [38].”
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“The authors of [31] extended it and created Relief-F, which can handle multi-class problems.”
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Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection
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“ReliefF, an improved algorithm of Relief, can deal with the feature selection problem for multi-class classification [24].”
BackgroundMethodFrom spreadsheets to sugar content modeling: A data mining approach
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“The parameters number of neighbors and number of iterations were kept as suggested in Robnik-Šikonja and Kononenko (2003) and Robnik-Šikonja et al. (1997): 10 and 100, respectively.”
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A Decision Tree-Based Attribute Weighting Filter for Naive Bayes
Hall, M. (2007) · Springer eBooks · doi:10.1007/978-1-84628-663-6_5 · Cited by 27
Neuroimaging features of whole‐brain functional connectivity predict attack frequency of migraine
Mu, J., Chen, T., Quan, S., Wang, C., Zhao, L., & Liu, J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24854 · Cited by 27
“The initial feature set first underwent feature dimension reduction using a ReliefF algorithm (Robnik-Šikonja & Kononenko, 2003), which reduced the feature dimension from 30,135 to 10,000.”
MethodSoftware fault localization using feature selection
Roychowdhury, S., & Sarfraz Khurshid, D. (2011) · doi:10.1145/2070821.2070823 · Cited by 27
“RRELIEF-F [23] is a further extension of RELIEF-F that can deal with with attributes having continuous values, and thus it can handle regression-like problems.”
MethodMulti-Label Feature Selection via Positive or Negative Correlation
Lin, Y., He, Z., Guo, L., & Ding, W. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3302653 · Cited by 26
“…in studying the correlation between features and labels, such as considering the intra-class and inter-class distance [46], large margin [47], consistency, etc. Unlike most of these indicators considered the absolute correlation between features and labels, a relative positive or…”
BackgroundZhang, H., Qin, X., Gao, X., et al. (2024) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2023.12.037 · Cited by 26
Hu, J., Sun, Y., & Qin, W. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102433 · Cited by 26
Effective feature selection using feature vector graph for classification
Zhao, G., Wu, Y. X., Chen, F., Zhang, J., & Bai, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.09.027 · Cited by 26
Zhang, Y., Wu, J., & Cai, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2549360 · Cited by 26
Chaieb, R., & Kalti, K. (2018) · Pattern Analysis and Applications · doi:10.1007/s10044-018-0760-x · Cited by 26
Han, X., Li, D., Liu, P., et al. (2019) · Soft Computing · doi:10.1007/s00500-019-04203-z · Cited by 26
Efficient deep discriminant embedding: Application to face beauty prediction and classification
Dornaika, F., Moujahid, A., Wang, K., & Feng, X. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103831 · Cited by 27
Influence of process parameters in AFP fiber steering on cylinders: Constant curvature paths
Wehbe, R. Y., Sacco, C., Radwan, A. B., et al. (2020) · Composites Part C Open Access · doi:10.1016/j.jcomc.2020.100036 · Cited by 26
Tuncer, T., Akbal, E., & Doğan, Ş. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102173 · Cited by 26
“Therefore, ReliefF is applied to extracted and normalized 4096 features.”
InfluentialGougam, F., Rahmoune, C., Benazzouz, D., et al. (2020) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/0954406220976154 · Cited by 26
Gharehbaghi, V., Farsangi, E. N., Yang, T. Y., et al. (2020) · Structures · doi:10.1016/j.istruc.2020.11.040 · Cited by 26
Dai, J., Liu, Y., & Chen, J. (2021) · Information Sciences · doi:10.1016/j.ins.2021.03.049 · Cited by 26
Zheng, Y., Chen, L., Liu, M., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.618604 · Cited by 26
“The algorithms of recursive feature elimination (RFE), ReliefF, and least absolute shrinkage and selection operator (LASSO) were used for feature selection in this study (25, 26).”
Method
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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