The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
Theoretical and Empirical Analysis of ReliefF and RReliefF
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,498
A Practical Approach to Feature Selection
Kira, K., & Rendell, L. A. (1992) · Elsevier eBooks · doi:10.1016/b978-1-55860-247-2.50037-1 · Cited by 3,090
Estimating attributes: Analysis and extensions of RELIEF
Kononenko, I. (1994) · Lecture notes in computer science · doi:10.1007/3-540-57868-4_57 · Cited by 3,062
Self-citationAn Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
The feature selection problem: traditional methods and a new algorithm
Kira, K., & Rendell, L. A. (1992) · National Conference on Artificial Intelligence · Cited by 1,828
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Dietterich, T. G. (1997). Machine learning research: Four current directions. AI Magazine, 18:4, 97-136.
- Jovanoski, V., & Lavrač, N. (1999). Feature subset selection in association rules learning systems. In M. Grobelnik, & D. Mladenič (Eds.), Prooceedings of the Conference Analysis, Warehousing and Mining the Data (AWAMIDA'99) (pp. 74-77).
- Robnik, M. (1995). Constructive induction in machine learning. Electrotehnical Review, 62:1, 43-49. (in Slovene).
- Robnik Šikonja, M. (1998). Speeding up relief algorithm with k-d trees. In Proceedings of Electrotehnical and Computer Science Conference (ERK'98) (pp. B:137-140). Portorož, Slovenia.
- Robnik Šikonja, M., & Kononenko, I. (1996). Context sensitive attribute estimation in regression. In M. Kubat, & G. Widmer (Eds.), Proceedings of ICML'96 Workshop on Learning in Context Sensitive Domains (pp. 43-52). Morgan Kaufmann.
- Vilalta, R. (1999). Understanding accuracy performance through concept characterization and algorithm analysis. In Proceedings of the ICML-99 Workshop on Recent Advances in Meta-Learning and Future Work (pp. 3-9).
Cited by
Sorted by citations
Items 401–500 of 1,004
Labory, J., Njomgue-Fotso, E., & Bottini, S. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.03.016 · Cited by 70
Manipulating Data and Dimension Reduction Methods: Feature Selection
Liu, H., & Zhao, Z. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1800-9_115 · Cited by 63
Adam, M., Oh, S. L., Sudarshan, V. K., Koh, J. E., Hagiwara, Y., Tan, J. H., Tan, R., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.018 · Cited by 64
mRMR-based feature selection for classification of cotton foreign matter using hyperspectral imaging
Jiang, Y., & Li, C. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.10.017 · Cited by 64
Ensemble Data Reduction Techniques and Multi-RSMOTE via Fuzzy Integral for Bug Report Classification
Guo, S., Chen, R., Wei, M., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2865780 · Cited by 62
Kalatian, A., & Farooq, B. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102962 · Cited by 62
“In mathematical terms, importance weight of covariate f , W f , is calculated based on the following equation (Robnik-Šikonja and Kononenko, 2003):”
MethodEwees, A. A., Ismail, F. H., & Sahlol, A. T. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118872 · Cited by 63
Gene Selection Using Locality Sensitive Laplacian Score
Liao, B., Jiang, Y., Liang, W., Zhu, W., Cai, L., & Cao, Z. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2328334 · Cited by 62
“We have conducted theoretical analysis on LSLS, and unlike traditional methods such as Relieff and KW, LSLS simultaneously considers both local margin structure and variance information.”
MethodInfluentialTensor-Based Multi-view Feature Selection with Applications to Brain Diseases
Cao, B., He, L., Kong, X., Yu, P. S., Hao, Z., & Ragin, A. B. (2014) · Proceedings · doi:10.1109/icdm.2014.26 · Cited by 62
“Considering feature selection, most of the existing studies can be categorized as filter models [17], [20] and embedded models based on sparsity regularization [7], [6], [26], [27].”
BackgroundA survey of feature selection methods for Gaussian mixture models and hidden Markov models
Adams, S., & Beling, P. A. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-017-9581-3 · Cited by 62
“The four methods are a brute search (testing all possible combinations of subsets contain the preselected number of features), a lite search (grouping the individual features with the best predictive ability into a single subset), a litelite search (similar to the lite search but ranking features), and the Relief algo…”
BackgroundMulti-Label Bioinformatics Data Classification With Ensemble Embedded Feature Selection
Guo, Y., Chung, F.-L., Li, G., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2931035 · Cited by 62
“The evaluation measures utilized by single-label feature selection methods can be χ 2 , Relief [25], COR [26] and mRMR [27].”
MethodA survey of machine learning approaches in animal behaviour
Kleanthous, N., Hussain, A. J., Khan, W., Sneddon, J. C., Al-Shamma'a, A. I., & Liatsis, P. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.10.126 · Cited by 68
Automated steel surface defect detection and classification using a new deep learning-based approach
Demir, K., Ay, M., Çavaş, M., & Demir, F. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08112-5 · Cited by 61
“The feature weights are obtained with the solution of the convex optimization problem [38].”
MethodOgaili, A. A. F., Jaber, A. A., & Hamzah, M. N. (2023) · Curved and Layered Structures · doi:10.1515/cls-2022-0214 · Cited by 61
“ReliefF is a supervised feature classi fi cation algorithm [44].”
MethodEhsani, M., Ostovari, M., Mansouri, S., et al. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.135331 · Cited by 61
Ontology-Based Meta-Mining of Knowledge Discovery Workflows
Hilario, M., Nguyen, P., Do, H., Woźnica, A., & Kalousis, A. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-20980-2_9 · Cited by 61
“This is the case of all so-called univariate methods, such as InfoGain, χ and SymmetricalUncertainty (see Figure 12), as well as a few multivariate methods like ReliefF [45,64] and SVMOne.”
MethodAmjady, N., & Keynia, F. (2009) · Energy Conversion and Management · doi:10.1016/j.enconman.2009.07.016 · Cited by 61
Quantifying and Visualizing Attribute Interactions: An Approach Based on Entropy
Jakulin, A., & Ivan Bratko, A. (2003) · Cited by 61
Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter
Biesiada, J., & Duch, W. (2008) · Advances in soft computing · doi:10.1007/3-540-32390-2_9 · Cited by 61
“A different way to find feature subsets is used in the Relief algorithm ([8] and [13]).”
MethodInfluentialLi, A., He, Z., & Zhang, Y. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.05.008 · Cited by 61
Sensitivity Analysis for Probabilistic Neural Network Structure Reduction
Kowalski, P. A., & Kusy, M. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2688482 · Cited by 61
“algorithm [49].”
Role of deep learning in infant brain MRI analysis
Mostapha, M., & Styner, M. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.06.009 · Cited by 63
“…machine learning techniques such as support vector regression (SVR) [96] combined with an adapted (rank prediction) reliefF feature filtering [97] showed ability to predict standardized scores of language ability at 24-months using a combination of behavioral measures (MSEL [98] and AOSI…”
MethodSTatistical Inference Relief (STIR) feature selection
Le, T. T., Urbanowicz, R. J., Moore, J. H., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty788 · Cited by 61
Assessment of Seismic Building Vulnerability from Space
Geiß, C., Taubenböck, H., Tyagunov, S., Tisch, A., Post, J., & Lakes, T. (2013) · Earthquake Spectra · doi:10.1193/121812eqs350m · Cited by 62
“We used enhanced approach “Relief-F” presented by Kononenko (1994), which allows for the handling multi-class data (Robnik-Šikonja and Kononenko 2003).”
MethodPNN for EEG-based Emotion Recognition
Zhang, J., Chen, M., Hu, S., Cao, Y., & Kozma, R. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844584 · Cited by 60
“Channel Selection ReliefF [14] is a widely used feature selection method in classification problems due to its effectiveness and simplicity of computation.”
MethodMoore, J. H. (2010) · Advances in genetics · doi:10.1016/b978-0-12-380862-2.00005-9 · Cited by 59
Ahn, S. J., Kwon, H., Yang, J., Park, M., Cha, Y. J., Suh, S. H., & Lee, J. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-65470-7 · Cited by 59
Dual space latent representation learning for unsupervised feature selection
Shang, R., Wang, L., Shang, F., et al. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107873 · Cited by 59
Peng, Y., Li, W., & Liu, Y. (2007) · PubMed · Cited by 59
Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis
Moore, J. H., & White, B. C. (2006) · Lecture notes in computer science · doi:10.1007/11844297_98 · Cited by 59
“This new ReliefF algorithm has been shown to be more robust to noisy attributes [ 21 ] and is widely used in data mining applications.”
MethodHan, B., Chen, X.-W., Talebizadeh, Z., & Xu, H. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-s3-s14 · Cited by 60
“Since MDR algo-rithm can not be applied to a large dataset directly, we first reduce the number of SNPs to 10 by ReliefF [34], a commonly-used feature selection algorithm, and then MDR performs an exhaustive search for a SNP set that can maximize cross-validation consistency and prediction accuracy.”
MethodA novel channel selection method for optimal classification in different motor imagery BCI paradigms
Shan, H., Xu, H., Zhu, S., & He, B. (2015) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0087-4 · Cited by 59
A Classification Framework Applied to Cancer Gene Expression Profiles
Hijazi, H., & Chan, C. (2013) · Journal of Healthcare Engineering · doi:10.1260/2040-2295.4.2.255 · Cited by 59
“, genes) [61], was employed as a filtering method to decrease the dimensionality of the feature space and to obtain a set of genes that can be used as an input to a second layer wrapper method (e.”
MethodAutomatic Intonation Recognition for the Prosodic Assessment of Language-Impaired Children
Ringeval, F., Demouy, J., Szaszák, G., Chétouani, M., Robel, L., Xavier, J., Cohen, D., & Plaza, M. (2010) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2010.2090147 · Cited by 60
Towards Automatic Generation of Metafeatures
Pinto, F., Soares, C., & Mendes‐Moreira, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31753-3_18 · Cited by 59
“ReliefF + PMR.”
MethodInfluentialA Composite Anomaly Detection System for Data-Driven Power Plant Condition Monitoring
Zhang, Y., Dong, Z. Y., Kong, W., & Meng, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2945366 · Cited by 59
“More details of the RELIEFF can be found in [24] and [25].”
BackgroundMethodMachine Learning Approaches for Early DRG Classification and Resource Allocation
Gartner, D., Kolisch, R., Neill, D. B., & Padman, R. (2015) · INFORMS Journal on Computing · doi:10.1287/ijoc.2015.0655 · Cited by 58
“Saeys et al. (2007) provide a literature review which covers several attribute selection methods employed in Bioinformatics.”
Achour, Y., Saidani, Z., Touati, R., et al. (2021) · Environmental Earth Sciences · doi:10.1007/s12665-021-09889-9 · Cited by 58
“In this research, the importance of these variables was assessed with considering the Relief F method (Robnik-Šikonja and Kononenko 2003).”
MethodAbed-alguni, B. H., Alawad, N. A., Al‐Betar, M. A., et al. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04201-z · Cited by 58
“ChiSquare [16], Gain Ratio [17], Information Gain [18], support vector machines [19], ReliefF [20, 21], and hybrid ReliefF [22, 23] are filter methods.”
MethodSankaran, A., Jain, A., Vashisth, T., Mayank Vatsa, A., & Singh, R. (2016) · Information Fusion · doi:10.1016/j.inffus.2016.05.002 · Cited by 58
“As studied by Robnik-ˇSikonja and Kononenko [30], RELIEF formulation can be optimized and modified by doing the following: 1.”
BackgroundPredicting species identity of bumblebees through analysis of flight buzzing sounds
Gradišek, A., Slapničar, G., Šorn, J., et al. (2016) · Bioacoustics · doi:10.1080/09524622.2016.1190946 · Cited by 58
“It is quite common approach in several machine learning communities to generate an abundant number of features, estimate their quality and construct a much smaller subset of most relevant attributes for further use (Robnik-Šikonja & Kononenko 2003).”
BackgroundExclusive Feature Learning on Arbitrary Structures via ell_1,2-norm
Kong, D., Fujimaki, R., Liu, J., et al. (2014) · neural information processing systems · Cited by 57
Czajkowski, M., & Krętowski, M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.07.019 · Cited by 56
Singh, S., Shreevastava, S., Som, T., et al. (2019) · Soft Computing · doi:10.1007/s00500-019-04228-4 · Cited by 57
A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds
He, B., Hou, F., Ren, C., Bing, P., & Xiangzuo, X. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.711225 · Cited by 61
“For the prediction of drug sensitivity, our commonly used fi ltering feature selection methods include: (1) The correlation coef fi cients between genomic features and output responses (30, 52); (2) ReliefF (31, 32) is general and successful attribute estimators.”
MethodDemir, F., & Taşçı, B. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11121276 · Cited by 59
“The feature weights are determined by solving the convex optimization problem [39].”
MethodMeng, L., Jiang, X., Liu, X., Fan, J., Ren, H., Guo, Y., Diao, H., Wang, Z., Chen, C., Dai, C., et al. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3217868 · Cited by 57
“adopted feature-ranking approaches were: 1) ReliefF [29]; 2) infinite latent feature selection (ILFS) [30]; 3) unsupervised”
MethodA Robust Gene selection Method for Microarray-based Cancer Classification
Wang, X., & Gotoh, O. (2010) · Cancer Informatics · doi:10.4137/cin.s3794 · Cited by 57
Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson’s disease
Khawaldeh, S., Tinkhauser, G., Shah, S. A., Petermann, K., Debove, I., Nguyen, T. A. K., Nowacki, A., Lachenmayer, M. L., Schüpbach, M. W. M., Pollo, C., et al. (2019) · Brain · doi:10.1093/brain/awz417 · Cited by 54
“The ReliefF feature selection algorithm was used to determine important features and eliminate non-important ones (Robnik-Šikonja and Kononenko, 2003).”
MethodMulti-stage dimension reduction for expensive sparse multi-objective optimization problems
Tan, Z., Wang, H., & Liu, S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.115 · Cited by 57
Attentional bias in MDD: ERP components analysis and classification using a dot-probe task
Li, X., Li, J., Hu, B., Zhu, J., Zhang, X., Wei, L., Zhong, N., Li, M., Ding, Z., Yang, J., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.07.003 · Cited by 59
Luo, F., Dong, Z. Y., Chen, G., et al. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2399698 · Cited by 56
Divergence-based feature selection for separate classes
Zhang, Y., Li, S., Wang, T., et al. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.06.036 · Cited by 56
“For Madelon dataset, DFSC and w DFSC also achieve better results than ReliefF and IG when the number of selected features is more than 5, although neither of them can select less than five features.”
MethodInfluentialCrack Detection in Earth Dam and Levee Passive Seismic Data Using Support Vector Machines
Fisher, W. D., Camp, T. K., & Krzhizhanovskaya, V. V. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.339 · Cited by 56
“Although the nine identified spectral features performed well during our clustering [1] and two-class SVM experiments (see Section 3.1), we applied the ReliefF [16] feature selection technique to further refine the feature set for use during our one-class SVM experiments.”
MethodInfluentialAssessing Motor Fluctuations in Parkinson’s Disease Patients Based on a Single Inertial Sensor
Pérez‐López, C., Samà, A., Rodríguez-Martín, D., Catalá, A., Cabestany, J., Aróstegui, J. M. M., de Mingo-Fernández, E., & Rodríguez‐Molinero, A. (2016) · Sensors · doi:10.3390/s16122132 · Cited by 60
Dahdouh, S., Andescavage, N. N., Yewale, S., Yarish, A., Lanham, D. C., Bulas, D., du Plessis, A. J., & Limperopoulos, C. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25806 · Cited by 56
Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge
Moore, J. H., & White, B. C. (2007) · Springer eBooks · doi:10.1007/978-0-387-49650-4_2 · Cited by 56
“This new ReliefF algorithm has been shown to be more robust to noisy attributes (Robnik-Sikonja and Kononenko, 2003) and is widely used in data mining applications.”
MethodCuesta–Frau, D., Murillo-Escobar, J. P., Orrego, D., & Delgado‐Trejos, E. (2019) · Entropy · doi:10.3390/e21040385 · Cited by 55
“In this paper, a relevance analysis routine based on the RELIEF-F algorithm was used to highlight the most discriminant patterns [60].”
MethodArefi, A., Sturm, B., von Gersdorff, G. J. E., et al. (2021) · LWT · doi:10.1016/j.lwt.2021.112297 · Cited by 55
A soft voting ensemble learning-based approach for multimodal sentiment analysis
Salur, M. U., & Aydın, İ. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07451-7 · Cited by 60
“After combining the features obtained from the text and images with the deep learning model, the feature selection was made with the ReliefF [36] feature selection algorithm.”
MethodEskandari, H., Saadatmand, H., Ramzan, M., et al. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123314 · Cited by 55
Ordinal Feature Selection for Iris and Palmprint Recognition
Sun, Z., Wang, L., & Tan, T. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2332396 · Cited by 55
“ReliefF is a simple yet efficient feature selection method suitable for problems with strong dependencies between features [12].”
BackgroundMethodJia, J., Yang, N., Zhang, C., et al. (2011) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2011.10.045 · Cited by 55
An Efficient Hybrid Feature Selection model for Dimensionality Reduction
Jain, D. K., & Singh, V. P. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.188 · Cited by 56
Defining Risk Factors for Red Man Syndrome in Children and Adults
Myers, A., Gaedigk, A., Dai, H. D., et al. (2012) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0b013e31824e10d7 · Cited by 55
“Further testing of possible gene–gene interactions was performed with multifactor dimensionality reduction (MDR) using previously described techniques.(25) MDR in conjunction with other data mining and machine learning techniques offers a strategy to detect possible nonlinear complex gene–gene interactions that is not…”
MethodOn Matching Finger-Selfies Using Deep Scattering Networks
Malhotra, A., Sankaran, A., Mayank Vatsa, A., et al. (2020) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2020.2999850 · Cited by 54
“In response selection, top-k responses are chosen based on Laplacian score for feature selection [11], Local Learning clustered feature selection [52], and ReliefF feature selection algorithm [36].”
MethodYasuda, T., Ookawara, S. A., Yoshikawa, S., & Matsumoto, H. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130069 · Cited by 55
Progressive Feature Transmission for Split Classification at the Wireless Edge
Lan, Q., Zeng, Q., Popovski, P., Gündüz, D., & Huang, K. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3221778 · Cited by 64
“Taking regression as an example, the well-known ReliefF estimator [24] can be adopted to measure feature importance, while the performance metric can be the meansquared error.”
MethodComparing state-of-the-art regression methods for long term travel time prediction
Mendes‐Moreira, J., Jorge, A., de Sousa, J. F., et al. (2012) · Intelligent Data Analysis · doi:10.3233/ida-2012-0532 · Cited by 54
“The Relief family of algorithms [27] weighs the features according to “how well their values distinguish between instances that are near to each other” [42].”
BackgroundMethodOlaharski, A. J., Gonzaludo, N., Bitter, H., GOLDSTEIN, D. M., Kirchner, S., Uppal, H., & Kolaja, K. L. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000446 · Cited by 54
A scalable approach to simultaneous evolutionary instance and feature selection
García‐Pedrajas, N., de Haro-García, A., & Pérez-Rodríguez, J. (2012) · Information Sciences · doi:10.1016/j.ins.2012.10.006 · Cited by 55
Performance Comparison of Feature Selection Methods
Phyu, T. Z., & Oo, N. N. (2016) · MATEC Web of Conferences · doi:10.1051/matecconf/20164206002 · Cited by 55
“This approach has shown good performance in various domains [6].”
BackgroundDimopoulos, T., Tyralis, H., Bakas, N., et al. (2018) · Advances in geosciences · doi:10.5194/adgeo-45-377-2018 · Cited by 54
“the declared price, a machine learning algorithm named Relieff (Robnik-Šikonja and Kononenko, 2003) was utilized.”
MethodInfluentialEnsemble residual network-based gender and activity recognition method with signals
Tuncer, T., Ertam, F., Doğan, Ş., Aydemir, E., & Pławiak, P. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03205-1 · Cited by 57
“The big weights imply distinctive features, and small weights describe redundant features [36, 37].”
BackgroundMethodDynamic interaction feature selection based on fuzzy rough set
Wan, J., Chen, H., Li, T., Yang, X., & Sang, B. (2021) · Information Sciences · doi:10.1016/j.ins.2021.10.026 · Cited by 54
Ant Colony Optimization for Genome-Wide Genetic Analysis
Greene, C. S., White, B. C., & Moore, J. H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87527-7_4 · Cited by 54
“This new ReliefF algorithm has been shown to be more robust to noisy attributes and missing data [19] and is widely used in data mining applications [ 19 ].”
MethodA portable HCI system‐oriented EEG feature extraction and channel selection for emotion recognition
Zheng, X., Liu, X., Zhang, Y., Cui, L., & Yu, X. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22295 · Cited by 53
“Although Relief algorithm owns a lot of advantages such as simple principles, fast computing speed and so on, 40 it has certain limitations that it cannot remove redundant features independently.”
BackgroundGalipeau, Y., Siragam, V., Laroche, G., Marion, E., Greig, M., McGuinty, M., Booth, R. A., Durocher, Y., Čuperlović‐Culf, M., Bennett, S. A. L., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103700 · Cited by 56
“Feature selection was performed using the machine learning method Relieff [38].”
InfluentialDifferential Evolution With Duplication Analysis for Feature Selection in Classification
Wang, P., Xue, B., Liang, J., & Zhang, M. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3213236 · Cited by 54
“ReliefF [32] and FOCUS [33] are two popular filter-based”
BackgroundAnalysis and Evaluation of Feature Selection and Feature Extraction Methods
Nogales, R., & Benalcázar, M. E. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00319-1 · Cited by 53
“It is necessary to mention that when k tends to one, the values of the weights are not reliable [28].”
BackgroundDeng, S., Wang, X., Zhu, Y., et al. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000814 · Cited by 53
Hakim, L., Fatma, R., & Novriandi, N. (2019) · International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE) · doi:10.1109/icomitee.2019.8920961 · Cited by 53
Evolutionary Machine Learning With Minions: A Case Study in Feature Selection
Zhang, N., Gupta, A., Chen, Z., & Ong, Y.-S. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3099289 · Cited by 54
Peng, Y., Li, W., & Liu, Y. (2006) · Cancer Informatics · doi:10.1177/117693510600200024 · Cited by 53
Quantifying and Visualizing Attribute InteractionsPreprint
Jakulin, A., & Ivan Bratko, A. (2003) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0308002 · Cited by 53
“is unable to appreciate their synergistic effect [34]. The disability of most feature selection algorithms to appreciate attributes involved in interactions was remedied with algorithms such as Relief [29, 47], which are usually based on instance-based heuristics. Besides, many learning algorithms are not able to take…”
MethodSODE: Self-Adaptive One-Dependence Estimators for classification
Wu, J. X., Pan, S., Xingquan Zhu, A., et al. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.023 · Cited by 52
“Examples include gain ratio [52], correlation-based algorithm [16], mutual information [24], and ReliefF attribute ranking algorithm [34].”
MethodInfluentialXu, D., Liu, P., & Chen, Z. P. (2021) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2021.108139 · Cited by 52
Wheat phenology detection with the methodology of classification based on the time-series UAV images
Zhou, M., Zheng, H., He, C. Q., et al. (2023) · Field Crops Research · doi:10.1016/j.fcr.2022.108798 · Cited by 52
Deng, S., Li, Y., Wang, J., et al. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110765 · Cited by 52
A hybrid feature selection method based on instance learning and cooperative subset search
Brahim, A. B., & Limam, M. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.10.005 · Cited by 52
“In diffuse large Bcells (DLBCL) presented by Shipp et al. (2002), the classification task is the prediction of the tissue types, where genes are used to discriminate DLBCL tissues from Follicular Lymphomas.”
BackgroundMethodInfluentialGene-gene interaction filtering with ensemble of filters
Yang, P., Ho, J. W. K., Yang, J. Y. H., & Zhou, B. B. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s1-s10 · Cited by 51
“This series of new methods, including ReliefF [17], tuned ReliefF (TuRF) [18], and Spatially Uniform ReliefF (SURF) [19] takes into account dependencies between attributes [17].”
MethodTay, B., Hyun, J. K., & Oh, S. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/276589 · Cited by 51
“Feature seLection (FeaLect) [11], Feature Selection based on a Distance Discriminant (FSDD) [12], ReliefF [13], Clearness-Based Feature Selection (CBFS) [14], and 𝑅 -value-based Feature Selection (RFS) [15] are well-known feature selection algorithms.”
MethodViolent activity detection with transfer learning method
Keçeli, A. S., & Kaya, A. (2017) · Electronics Letters · doi:10.1049/el.2017.0970 · Cited by 52
An efficient hour-ahead electrical load forecasting method based on innovative features
Rafati, A., Joorabian, M., & Mashhour, E. (2020) · Energy · doi:10.1016/j.energy.2020.117511 · Cited by 52
Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting
Wu, Z., Wang, X., & Jiang, B. (2020) · Applied Sciences · doi:10.3390/app10093258 · Cited by 52
“The ReliefF algorithm is an improvement on the Relief algorithm and is mainly used for feature selection of multi-class labels [18,19].”
MethodZhang, Y., Zhu, R., Chen, Z., et al. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.09.028 · Cited by 52
“For each dataset, the Wilcoxon rank-sum test is conducted to evaluate the statistical significance of the difference between two sequences of the samples of classification results, i.e., the sequence of the result 2 ReliefF does not belong to the linear span shown in Eq.”
InfluentialCheng, C., Kao, Y.-F., & Lin, H.-P. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107487 · Cited by 52
A Survey on Supervised Classification on Data Streams
Lemaire, V., Salperwyck, C., & Bondu, A. (2015) · Lecture notes in business information processing · doi:10.1007/978-3-319-17551-5_4 · Cited by 56
“Two main topics of research exists in literature: i) the speed up of the search of the KNN [40, 41]; ii) the learning of metrics [42–44].”
BackgroundFeature Selection Boosted by Unselected Features
Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., & Yang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3058172 · Cited by 51
“Filter-based feature selection methods, such as fisher-score [4], ReliefF [14], minimum redundancy maximum relevance (mRMR) [15], and information gain (infogain) [16], [25], estimate the relevance of a feature independently of the perspectives of statistics or information theory.”
MethodA new feature selection method based on symmetrical uncertainty and interaction gain
Lin, X., Li, C., Ren, W., Luo, X., & Qi, Y. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.107149 · Cited by 51
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
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
