Cited by 3,025Data Mining Algorithms and ApplicationsMachine Learning and Data ClassificationRough Sets and Fuzzy Logic

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Theoretical and Empirical Analysis of ReliefF and RReliefF

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Robnik-Šikonja, Marko, Kononenko, Igor·2003·Machine Learning

DOI 10.1023/a:1025667309714

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“[25], relief and its extension [23], [24], and sequential forward generation [59] are examples of feature selection algorithms.”
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Ke, W., Wu, C., Wu, Y., & Xiong, N. N. (2018) · IEEE Access · doi:10.1109/access.2018.2873634 · Cited by 50
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“Alongside the proposed algorithm, the datasets were tested against a few existing feature selection methods such as ReliefF, Recursive Feature Elimination (RFE), and Cuckoo search optimization (CSO) algorithm [54-56].”
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“The details of the Relief-F feature selection algorithm can be found in publications [44 – 46].”
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“These are the t-test [20], and the Relieff algorithms [21] (see Figure 3).”
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“The ReliefF algorithm ranks the features according to its the highest correlation with the observed class while taking into account the distances between opposite classes (Robnik-Sikonja & Kononenko, 2003).”
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“This table compares the proposed method with other algorithms traditionally used for this task, namely principal component analysis (PCA) [20] and ReliefF [38].”
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“…set of binary features: each discrete feature A with the set of values {v0, v1 . . . , vn} was transformed to n numerical binary features A1, . . . , An, where Ai = 1 (and Aj = 0 for j = 1, . . . , n; j = i) denotes that the value of the original feature was A = vi (Ai = 0 for i = 1, . . . , n…”
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“RReliefF algorithm RReliefF algorithm is a feature selection method based on statistical correlation (Robnik-Sikonja and Kononenko, 2003).”
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“Disguised values can lead to biased outcomes, reduced accuracy, and misinterpretation of AI models.”
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“The relieff [30] algorithm was used to determine the importance of features.”
Method
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“…available and accepted algorithms for feature selection: (a) mutual information (Zaffalon & Hutter, 2002), (b) Fisher criterion (Gu, Li, & Han, 2011), (c) Relief-F algorithm (Robnik-Šikonja & Kononenko, 2003), and (d) minimal redundancy maximal relevance (mRMR) criterion (Peng, Long, & Ding, 2005).”
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“To estimate the relevance and weight to the classification among the 34 textural features, ReliefF was used as a feature selection method (Robnik-Šikonja and Kononenko 2003) (see Appendix A).”
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“, (2)-test, Fisher score, ReliefF [8], and so forth, but the unsupervised counterparts become more challenging because of no true class labels available in the feature selection.”
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“The first compared method is ReliefF (Robnik-Š ikonja and Kononenko 2003), a supervised feature estimation method, and the second one is WaLuDi (Martinez-Uso et al. 2007), an unsupervised clustering-based method.”
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“In order to validate the effectiveness of the proposed method, we performed experiments using a comparison with the state-of-art of Feature Selection methods (FS) including Student’s t-Test analysis [39], Fisher score [86], [87], Support vectorMachine feature elimination (SVM-RFE) [88], feature selection with Random F…”
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“Theoretical and em- Sikonja and I. Kononenko. pirical analysis of ReliefF and RReliefF.”
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“Many ranking criteria for supervised feature selection have been proposed, for instance, mutual information [32], information gain [44], Gini index [31], distance measure [29], j-measure [39], Relief algorithm and its extensions [35], MDL [22], 2 statistic [12], and G statistic [7].”
Background
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Bridging Feature Selection and Extraction: Compound Feature Generation
Sreevani, & Murthy, C. A. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2619712 · Cited by 46
“According to Knn (K=1) classifier, SMPeMR3 obtains 6.9, 11.0, 4.5, 19.0, 6.1, 13.0 percent average improvements in comparison with feature selection methods Fisher, ReliefF, DISR, SPEC, TR and MRSF respectively, and with feature extraction methods LDA, MMC, ALDE, MFA, QMI and SLDA, it obtains 5.8, 2.2, 1.2, 10.0, 10.7…”
BackgroundInfluential
Granger Causality Driven AHP for Feature Weighted kNN
Bhattacharya, G., Ghosh, K., & Chowdhury, A. S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.01.018 · Cited by 45
“RELIEFF [23], the extended version of RELIEF [22] is robust and efficient to deal with incomplete and noisy data [34].”
Background
Context-aware obstacle detection for navigation by visually impaired
Gharani, P., & Karimi, H. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.06.002 · Cited by 45
“Generally, feature selection algorithms may use measurement of the various characteristics of the training data such as distance, consistency, dependency, information, and correlation (Kira and Rendell, 1992; Robnik-Siknja and Kononeko, 2003; Tang et al., 2014).”
BackgroundMethod
Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation
Chowdhury, A. S., Call, D. R., & Broschat, S. L. (2019) · Scientific Reports · doi:10.1038/s41598-019-50686-z · Cited by 46
“For RReliefF, we considered 5 neighbors and 30 instances as suggested in a previous study 35 .”
Method
Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting
Huang, N., Wang, W., Wang, S., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2971033 · Cited by 45
“The function diff( ) [31] is used for calculating the distance between instances to find the nearest neighbors.”
Method
Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach
Gharehbaghi, V., Nguyễn, A., Farsangi, E. N., & Yang, T. Y. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101292 · Cited by 45
Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm
Sharifai, A. G., & Zainol, Z. B. (2020) · Genes · doi:10.3390/genes11070717 · Cited by 45
Supervised feature selection through Deep Neural Networks with pairwise connected structure
Huang, Y., Jin, W., Yu, Z., & Li, B. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106202 · Cited by 45
Feature selection for high-dimensional data in astronomy
Zheng, H., & Zhang, Y. (2007) · Advances in Space Research · doi:10.1016/j.asr.2007.08.033 · Cited by 45
Driver drowsiness detection in video sequences using hybrid selection of deep features
Bekhouche, S. E., Ruichek, Y., & Dornaika, F. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109436 · Cited by 44
Mean based relief: An improved feature selection method based on ReliefF
Aggarwal, N., Shukla, U., Saxena, G. J., Rawat, M., Bafila, A. S., Singh, S., & Pundir, A. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04662-w · Cited by 51
“ReliefF variant named RReliefF [24], formulated for regression problems, uses relative distances between the predicted values of pairs of instances and is computationally more expensive than ReliefF.”
Method
A novel caps-EEGNet combined with channel selection for EEG-based emotion recognition
Chen, K., Jing, H., Liu, Q., Ai, Q., & Ma, L. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105312 · Cited by 45
Feature subset selection with cumulate conditional mutual information minimization
Zhang, Y., & Zhang, Z. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.003 · Cited by 44
“In addition, for each of the three classifiers, best results are achieved by applying 1NN, which show that CCM wins in eight cases and only losses in one case in comparison with both CFS and FCBF, and wins in seven cases and losses in two cases in comparison with ReliefF.”
MethodInfluential
Scalable Feature Selection via Distributed Diversity Maximization
Zadeh, S. A., Ghadiri, M., Mirrokni, V., et al. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10926 · Cited by 44
“…(Nguyen et al. 2014), Quadratic Programming Feature Selection (QPFS) (Rodriguez-Lujan et al. 2010), and Conditional Mutual Information (CMI)(Cheng et al. 2008) as baselines as well as non MI-based methods, fisher score (Duda, Hart, and Stork 2001) and reliefF (Robnik-Šikonja and Kononenko 2003).”
Method
Multiple features extraction and selection for detection and classification of stator winding faults
Haroun, S., Seghir, A. N., & Touati, S. (2017) · IET Electric Power Applications · doi:10.1049/iet-epa.2017.0457 · Cited by 44
Classification of vocal and non-vocal segments in audio clips using genetic algorithm based feature selection (GAFS)
Murthy, Y. V. S., & Koolagudi, S. G. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.005 · Cited by 44
Machine learning technology in the application of genome analysis: A systematic review
Wu, J., & Zhao, Y. (2019) · Gene · doi:10.1016/j.gene.2019.04.062 · Cited by 47
Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach
Naik, D. L., Sajid, H. U., & Kiran, R. (2019) · Metals · doi:10.3390/met9050546 · Cited by 44
“A more detailed description of the algorithm can be found elsewhere [44,46].”
BackgroundMethod
Robust Flexible Feature Selection via Exclusive L21 Regularization
Ming, D., & Ding, C. H. Q. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/438 · Cited by 45
White blood cell type identification using multi-layer convolutional features with an extreme-learning machine
Khan, A. H., Eker, A., Chefranov, A. G., & Demirel, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102932 · Cited by 45
Classification of complete blood count and haemoglobin typing data by a C4.5 decision tree, a naïve Bayes classifier and a multilayer perceptron for thalassaemia screening
Setsirichok, D., Piroonratana, T., Wongseree, W., et al. (2011) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.03.007 · Cited by 44
Multi-test decision tree and its application to microarray data classification
Czajkowski, M., Grześ, M., & Krętowski, M. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.01.005 · Cited by 45
Manipulating Data and Dimension Reduction Methods: Feature Selection
Liu, H., & Zhao, Z. (2009) · Encyclopedia of Complexity and Systems Science · doi:10.1007/978-0-387-30440-3_317 · Cited by 44
Malware Dataset Generation and Evaluation
Borah, P., Bhattacharyya, D., & Kalita, J. K. (2020) · doi:10.1109/cict51604.2020.9312053 · Cited by 43
“The ranks are calculated using three feature selectors [7] [8] [9].”
Method
Early prediction of cycle life for lithium-ion batteries based on evolutionary computation and machine learning
Gong, D., Gao, Y., Kou, Y., & Wang, Y. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104376 · Cited by 44
A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting
Niu, T., Li, J., Wei, W., et al. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119964 · Cited by 43
The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics
Greene, C. S., Himmelstein, D., Kiralis, J., & Moore, J. H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12211-8_16 · Cited by 43
“In 2003 Robnik-Sikonja and Kononenko [27] performed a comprehensive analysis and determined that ten neighbors was an appropriate number for ReliefF, so we use ten neighbors here.”
BackgroundMethod
Characterizing the presence of oilseed rape feral populations on field margins using machine learning
Pivard, S., Demšar, D., Lecomte, J., et al. (2008) · Ecological Modelling · doi:10.1016/j.ecolmodel.2007.10.012 · Cited by 43
Mapping microarray gene expression data into dissimilarity spaces for tumor classification
García, V., & Sánchez, J. S. (2014) · Information Sciences · doi:10.1016/j.ins.2014.09.064 · Cited by 43
“Gene selection with ReliefF ReliefF is an improved version of the Relief procedure to estimate the quality of features in problems with strong dependencies between features [18,33].”
Method

Authors: Robnik-Šikonja, M., & Kononenko, I.
Affiliations: University of Ljubljana (SI)
ORCID: Kononenko, Igor
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 2003-10
Volume: 53
Issue: 1-2
Pages: 23-69
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1023/a:1025667309714
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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