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Theoretical and Empirical Analysis of ReliefF and RReliefF
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Items 201–300 of 1,004
Moncada‐Torres, A., Leuenberger, K., Gonzenbach, R. R., Luft, A. R., & Gassert, R. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/7/1245 · Cited by 113
“The assigned weight ranges from −1 to 1, with large positive values given to important attributes (Robnik-Sikonja and Kononenko 2003).”
BackgroundFingolimod modulates multiple neuroinflammatory markers in a mouse model of Alzheimer’s disease
Aytan, N., Choi, J., Carreras, I., Brinkmann, V., Kowall, N. W., Jenkins, B. G., & Dedeoglu, A. (2016) · Scientific Reports · doi:10.1038/srep24939 · Cited by 111
Feature Interaction for Streaming Feature Selection
Zhou, P., Li, P., Zhao, S. G., & Wu, X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3025922 · Cited by 114
A human study of patch maintainability
Fry, Z. P., Landau, B., & Weimer, W. R. (2012) · International Symposium on Software Testing and Analysis · doi:10.1145/2338965.2336775 · Cited by 113
“We used the ReliefF technique to measure a given feature’s relative predictive power, which computes this statistic without assuming conditional independence [29].”
MethodHu, Q., Pan, W., An, S., Ma, P., & Wei, J. (2010) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-010-0008-6 · Cited by 113
“In addition, we try to select genes with ReliefF.”
MethodInfluentialA hybrid ensemble-filter wrapper feature selection approach for medical data classification
Singh, N., & Singh, P. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104396 · Cited by 115
Zhu, Z., Jia, S., He, S., et al. (2014) · Information Sciences · doi:10.1016/j.ins.2014.11.045 · Cited by 111
“In line with the study on hyperspectral remote sensing data, the performance of M3GDFE on hyperspectral face data is compared with that of ALL, MI, ReliefF, PCA, and G3GDFE.”
InfluentialA Noncontact Breathing Disorder Recognition System Using 2.4-GHz Digital-IF Doppler Radar
Zhao, H., Hong, H., Miao, D., Li, Y., Zhang, H., Zhang, Y., Li, C., & Zhu, X. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2817258 · Cited by 112
“The ReliefF algorithm can provide a unified view on the attribute estimation [52].”
MethodSupervised feature selection: A tutorial
Huang, S. H. (2015) · Artificial Intelligence Research · doi:10.5430/air.v4n2p22 · Cited by 116
Bui, D. T., Le, V. H., & Hoang, N. (2018) · Ecological Informatics · doi:10.1016/j.ecoinf.2018.08.008 · Cited by 115
Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11172676 · Cited by 110
“MLAs such as KNN [123–126], SVM [127–129], decision tree [130,131], AdaBoost [132–134] random forest [135–138], naïve Bayes [139,140], regularization [141–143] and relief-F [144–147], entropy evaluation criteria [148] are extensively used for identifying the most relevant variables in the datasets.”
BackgroundImproving strategies for detecting genetic patterns of disease susceptibility in association studies
Calle, M. L., Urrea, V., Vellalta, G., et al. (2008) · Statistics in Medicine · doi:10.1002/sim.3431 · Cited by 109
“Comparison with other filter methods that have been proposed for exploring interactions, such as ReliefF [12, 28 ], would be of interest.”
MethodInvariant optimal feature selection: A distance discriminant and feature ranking based solution
Liang, J., Yang, S., & Winstanley, A. C. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.018 · Cited by 109
“Among these methods, ReliefF [17] is a special one, which is indeed a feature ranking algorithm.”
MethodBankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets
Zoričák, M., Gnip, P., Drotár, P., & Gazda, V. (2019) · Economic Modelling · doi:10.1016/j.econmod.2019.04.003 · Cited by 121
Bostani, H., & Sheikhan, M. (2015) · Soft Computing · doi:10.1007/s00500-015-1942-8 · Cited by 108
“…improved the performance of feature selection as compared to the several standard wrapper-based (e.g., BGSA and BPSO) and filter-based [e.g., ReliefF (Robnik-Šikonja and Kononenko 2003), mRMR (Peng et al. 2005), MIFS-U (Kwak and Choi 2003), and Chi-square (Liu and Setiono 1995)] feature selection…”
MethodInfluentialGene selection for tumor classification using neighborhood rough sets and entropy measures
Chen, Y., Zhang, Z., Zheng, J., Ma, Y., & Xue, Y. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.02.007 · Cited by 109
Sparse Graph Embedding Unsupervised Feature Selection
Wang, S., & Zhu, W. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2605132 · Cited by 107
“…typically supervised feature selection methods are based on information entropy [2], mutual information [3], [4], Pearson correlation coefficients [5], Fisher score [6], ReliefF [7], trace ratio [8], spectral analysis [9], Laplacian score (LS) [10], and Hilbert Schmidt independence criterion [11].”
MethodZaghloul, M. S., & Achari, G. (2022) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2022.107430 · Cited by 107
Searching for interacting features in subset selection1
Zhao, Z., & Liu, H. (2009) · Intelligent Data Analysis · doi:10.3233/ida-2009-0364 · Cited by 107
Feature selection using dynamic weights for classification
Sun, X., Liu, Y., Xu, M., Chen, H., Han, J., & Wang, K. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.10.001 · Cited by 107
“For ReliefF, we use five neighbors and 30 instances throughout the experiments as suggested by Robnik-Šikonja and Kononenko [42], which were also used in the literature [21].”
MethodFeature Selection Based on Dependency Margin
Liu, Y., Tang, F., & Zeng, Z. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2347372 · Cited by 106
“We then apply four feature selection approaches to find the most relevant features,11 i.e., ReliefF, consistency, dependency, WFSDMS, into the face recognition process, and try to select the proper subset of face (image) regions.”
BackgroundInfluentialFeature Selection for Monotonic Classification
Hu, Q., Pan, W., Lei Zhang, A., Zhang, D. D., Song, Y., Guo, M., & Yu, D. (2011) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2011.2167235 · Cited by 106
“Mutual information (MI) [28]–[31], dependence [32]–[36], consistency [37], [38], [54], distance [39], and classification margin [40]–[42] were introduced or developed as metrics of feature quality in feature selection.”
MethodMeng, X., Jiang, R., Lin, D., Bustillo, J., Jones, T., Chen, J., Yu, Q., Du, Y., Zhang, Y., Jiang, T., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.026 · Cited by 106
“Finally, the weight for each feature A is defined as: W A½ ¼ NdC&dA A½ NdC − NdA A½ −NdC&dA A½ m−NdC ð1Þ While, the detailed updates for Ndc, NdA(A) and NdA(A)dC, as well as the Pseudo code of RReliefF can be found in (Robnik-Šikonja and Kononenko, 2003).”
Fisher, W. D., Camp, T. K., & Krzhizhanovskaya, V. V. (2016) · Journal of Computational Science · doi:10.1016/j.jocs.2016.11.016 · Cited by 106
Tuncer, T., Doğan, Ş., & Subaşı, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102591 · Cited by 106
A graph theoretic approach for unsupervised feature selection
Moradi, P., & Rostami, M. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.05.005 · Cited by 106
“ReliefF (Robnik-Sikonja and Kononenko, 2003) is an extension of the Relief algorithm, which applies a feature weighting scheme and searches for several nearest neighbors.”
MethodChen, K., Xue, B., Zhang, M., et al. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3134804 · Cited by 106
“…(embedded feature selection method) [55], FWDT (tree-based feature selection method) [56], ReliefF (univariate feature selection method) [38], CFS (statistical-based feature selection method) [57], FCBF (correlation-based feature selection method) [58], and mRMR (information-based…”
MethodInfluentialLi, L., Xuan, M., Lin, Q., Jiang, M., Ming, Z., & Tan, K. C. (2023) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2023.3254155 · Cited by 108
“In particular, three common filtering methods are adopted in this article to determine the importance of features, namely, the Relief -F [3], term variance (TV) [5], and Pearson correlation coefficient (PCC) methods [4].”
BackgroundMethodInfluentialZhang, X., Zhang, Q., Chen, M., Sun, Y., Qin, X., & Li, H. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.11.016 · Cited by 104
Time-varying hierarchical chains of salps with random weight networks for feature selection
Faris, H., Heidari, A. A., Al-Zoubi, A. M., Mafarja, M. M., Aljarah, I., Eshtay, M., & Mirjalili, S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112898 · Cited by 105
Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN
Sánchez, R., Lucero, P., Vásquez, R. E., et al. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169526 · Cited by 103
A hybrid Artificial Immune optimization for high-dimensional feature selection
Zhu, Y., Li, W., & Li, T. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110111 · Cited by 106
Survey:Time-series data preprocessing: A survey and an empirical analysis
Tawakuli, A., Havers, B., Gulisano, V. M., Kaiser, D., & Engel, T. (2024) · Journal of Engineering Research · doi:10.1016/j.jer.2024.02.018 · Cited by 144
OFS-Density: A novel online streaming feature selection method
Zhou, P., Hu, X., Li, P., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.08.009 · Cited by 102
Online streaming feature selection using adapted Neighborhood Rough Set
Zhou, P., Hu, X., Li, P., & Wu, X. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.074 · Cited by 104
Detection of Depression and Scaling of Severity Using Six Channel EEG Data
Mahato, S., Goyal, N., Ram, D., & Paul, S. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01573-y · Cited by 104
“Kira and Reindell has proposed the relief algorithm [36, 37].”
BackgroundA Unified View of Causal and Non-causal Feature Selection
Yu, K., Liu, L., & Li, J. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3436891 · Cited by 101
“In the past decades, different evaluation criteria have been proposed, such as those based on distance [38], mutual information [41], dependency [42], and consistency [14].”
BackgroundA bi-stage feature selection approach for COVID-19 prediction using chest CT images
Sen, S., Saha, S., Chatterjee, S., Mirjalili, S., & Sarkar, R. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02292-8 · Cited by 103
Araúzo-Azofra, A., Aznarte, J. L., & Benítez, J. M. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.12.160 · Cited by 101
“Based on Relief-F analysis (Robnik-Sikonja & Kononenko, 2003) and some preliminary experiments, the number of neighbors to search is set to 6, and the number of instances to sample is set to 100.”
MethodFast and Robust Attribute Reduction Based on the Separability in Fuzzy Decision Systems
Hu, M., Tsang, E. C. C., Guo, Y., & Xu, W. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3040803 · Cited by 100
“To verify the effectiveness and feasibility of the proposed algorithm (SFSS), we compare it with HANDI [1], NMI [3], RELIEF-F [11], and mRMR [45].”
MethodFeature Selection: A Review and Comparative Study
Bouchlaghem, Y., Akhiat, Y., & Amjad, S. (2022) · E3S Web of Conferences · doi:10.1051/e3sconf/202235101046 · Cited by 118
“Relief-F is implemented to solve multi-class issues [24].”
MethodFeature selection based on feature interactions with application to text categorization
Tang, X., Dai, Y., & Xiang, Y. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.018 · Cited by 99
Streaming feature selection algorithms for big data: A survey
Nuaimi, N. A., Masud, M. M., Serhani, M. A., & Zaki, N. (2019) · Applied Computing and Informatics · doi:10.1016/j.aci.2019.01.001 · Cited by 99
A modified feature selection method based on metaheuristic algorithms for speech emotion recognition
Yıldırım, S., Kaya, Y., & Kılıç, F. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107721 · Cited by 98
Vommi, A. M., & Battula, T. K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119612 · Cited by 102
Self-adaptive attribute weighting for Naive Bayes classification
Wu, J. X., Pan, S., Xingquan Zhu, A., et al. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.09.019 · Cited by 98
“…methods to evaluate the importance of attributes, including gain ratio (Zhang & Sheng, 2004), correlation-based algorithm (Hall (2000)), mutual information (Jiang, Zhang, & Cai, 2009; Jiang, Zhang, Cai, & Wang, 2012b), and ReliefF attribute ranking algorithm (Robnik-Šikonja & Kononenko, 2003).”
MethodLuo, F., Zhao, J., Dong, Z. Y., et al. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2452293 · Cited by 97
“Then, 20 critical features are selected by employing the RELIEF algorithm [74].”
Methodvan der Linden, J. H., Narsilio, G. A., & Tordesillas, A. (2016) · Physical review. E · doi:10.1103/physreve.94.022904 · Cited by 97
Hoang, N., Chen, C.-T., & Liao, K. (2017) · Measurement · doi:10.1016/j.measurement.2017.08.031 · Cited by 97
Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach
Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm8010012 · Cited by 99
“Kononenko [41] proposed the ReliefF method, which is based on Relief, to solve multi-class classification.”
MethodInfluentialEnsemble feature selection using election methods and ranker clustering
Drotár, P., Gazda, M., & Vokorokos, L. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.033 · Cited by 97
Short-term forecasting of natural gas prices using machine learning and feature selection algorithms
Čeperić, E., Žiković, S., & Čeperić, V. (2017) · Energy · doi:10.1016/j.energy.2017.09.026 · Cited by 100
Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM
Laporte, L., Flamary, R., Canu, S., Dejean, S., & Mothe, J. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2286696 · Cited by 94
Automatic grape bunch detection in vineyards with an SVM classifier
Liu, S., & Whitty, M. A. (2015) · Journal of Applied Logic · doi:10.1016/j.jal.2015.06.001 · Cited by 96
“In order to simplify the classifier model, reduce memory usage and improve the computational speed, the ReliefF algorithm [17] and sequential feature selection [10] were applied for decreasing the feature dimensions.”
MethodDefect classification based on deep features for railway tracks in sustainable transportation
Aydın, İ., Akın, E., & Karaköse, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107706 · Cited by 96
Jaw, E., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13101764 · Cited by 110
Attallah, O. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108798 · Cited by 100
Georga, E. I., Protopappas, V. C., Polyzos, D. K., & Fotiadis, D. I. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1263-1 · Cited by 104
Stable Gene Selection from Microarray Data via Sample Weighting
Yu, L., Han, Y., & Berens, M. E. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.47 · Cited by 97
“Our experiments on a set of public microarray data sets show that the proposed sample weighting framework significantly improves the stability of SVM-RFE [15] and ReliefF [ 29 ] while maintaining their classification performance.”
MethodInfluentialApplication of machine learning to predict obstructive sleep apnea syndrome severity
Mencar, C., Gallo, C., Mantero, M., Tarsia, P., Carpagnano, G. E., Barbaro, M. P. F., & Lacedonia, D. (2019) · Health Informatics Journal · doi:10.1177/1460458218824725 · Cited by 102
A systematic review of multi-label feature selection and a new method based on label construction
Spolaôr, N., Monard, M. C., Tsoumakas, G., & Lee, H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.118 · Cited by 94
“This 330 is a commonly-used value that leads lazy learning algorithms to achieve sat331 isfactory results [20, 29].”
BackgroundMa, C., Zhang, P., Song, F., Sun, Y., Fan, G., Zhang, T., Feng, Y., & Zhang, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181328 · Cited by 100
“[33] M. Robnik-Šikonja and I. Kononenko, “Theoretical and empirical analysis of ReliefF and RReliefF,” Mach.”
MethodA feature selection approach based on NSGA-II with ReliefF
Xue, Y., Zhu, H., & Neri, F. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.109987 · Cited by 94
Statistical analysis of proteomics data: A review on feature selection
Lualdi, M., & Fasano, M. (2019) · Journal of Proteomics · doi:10.1016/j.jprot.2018.12.004 · Cited by 93
A tutorial-based survey on feature selection: Recent advancements on feature selection
Moslemi, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107136 · Cited by 96
Feature evaluation and selection with cooperative game theory
Sun, X., Liu, Y., Li, J., Zhu, J., Chen, H., & Liu, X. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.02.001 · Cited by 94
Yang, P., Zhou, B. B., Zhang, Z., & Zomaya, A. Y. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s1-s5 · Cited by 92
“Examples are c-statistic [3], t-statistic [4], ReliefF [5], Information Gain, and Gain Ratio [6].”
Subspace learning-based graph regularized feature selection
Shang, R., Wang, W., Stolkin, R., & Jiao, L. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.09.006 · Cited by 91
Wrapper-based gene selection with Markov blanket
Wang, A., An, N., Yang, J., Chen, G., Li, L., & Alterovitz, G. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.12.002 · Cited by 90
“Further, commonly used filter metrics include distance-, dependency-, consistency-, and information theorybased metrics [14,16,17].”
MethodDang, V.-H., Hoang, N., Nguyen, L.-M.-D., et al. (2020) · Forests · doi:10.3390/f11010118 · Cited by 91
“…slope was the most relevant factor for spatial mapping of landslide susceptibility in the study area, followed by SPI and elevation. variables expressing the discriminative power of each variable used for data classification influencing factors was preliminarily evaluated by the ReliefF method [59].”
MethodInfluentialVMFS: A VIKOR-based multi-target feature selection
Hashemi, A., Dowlatshahi, M. B., & Nezamabadi–pour, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115224 · Cited by 91
Kumar, D., Meghwani, S. S., & Thakur, M. (2016) · Journal of Computational Science · doi:10.1016/j.jocs.2016.07.006 · Cited by 92
Multi-Feature Fusion Approach for Epileptic Seizure Detection From EEG Signals
Radman, M., Moradi, M., Chaibakhsh, A., Kordestani, M., & Saif, M. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3026032 · Cited by 90
“In contrast, the requirement for selecting a threshold is its drawback [41].”
BackgroundWang, J., Lv, M., Li, Z., & Zeng, B. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119129 · Cited by 91
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“Today, one of the main problems in machine learning and statistics is keeping track of the most relevant information.”
BackgroundTsanas, A. (2012) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-w4ky0ynwe · Cited by 92
A new hybrid filter/wrapper algorithm for feature selection in classification
Zhang, J., Xiong, Y., & Min, S. (2019) · Analytica Chimica Acta · doi:10.1016/j.aca.2019.06.054 · Cited by 89
Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
Park, H., & Son, J. (2021) · Sensors · doi:10.3390/s21041186 · Cited by 93
“The Relief algorithm is a method for determining important features in binary classification [86].”
BackgroundMethodInfluentialA Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection
Islam, M. R., Lima, A. A., Das, S. C., Mridha, M. F., Prodeep, A. R., & Watanobe, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3205618 · Cited by 96
A survey on online feature selection with streaming features
Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-5489-3 · Cited by 88
“Some popular criteria include distance measures [43, 44], information measures, dependency measures, and consistency measures [45, 46].”
BackgroundAn improved feature selection algorithm based on graph clustering and ant colony optimization
Ghimatgar, H., Kazemi, K., Helfroush, M. S., & Aarabi, A. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.025 · Cited by 88
Duch, W. (2008) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-35488-8_4 · Cited by 88
McDonald, M., Christoforidou, E., van Rijsbergen, N. J., Gajwani, R., Groß, J., Gumley, A. I., Lawrie, S. M., Schwannauer, M., Schultze‐Lutter, F., & Uhlhaas, P. J. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby069 · Cited by 87
Singh, J., Azamfar, M., Li, F., & Lee, J. (2020) · Measurement Science and Technology · doi:10.1088/1361-6501/ab8df9 · Cited by 89
“ReliefF selects the most important features by estimating the quality of attributes according to how well their values distinguish between instances that are near to each other.”
MethodTracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology
Patel, S., Hughes, R., Hester, T., Stein, J. M., Akay, M., Dy, J., & Bonato, P. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626446 · Cited by 87
“A closer look at the ranking provided by the ReliefF algorithm for the manipulation tasks revealed that the features from segment S3 were ranked the lowest while that from segment S2 were ranked highest on average.”
MethodInfluentialA Variance Minimization Criterion to Feature Selection Using Laplacian Regularization
He, X., Ji, M., Zhang, C., & Bao, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.44 · Cited by 87
Pirgazi, J., Alimoradi, M., Abharian, T. E., & Olyaee, M. H. (2019) · Scientific Reports · doi:10.1038/s41598-019-54987-1 · Cited by 88
Li, A., Xue, B., & Zhang, M. (2020) · Information Sciences · doi:10.1016/j.ins.2020.03.032 · Cited by 86
Interpretation of Depression Detection Models via Feature Selection Methods
Alghowinem, S., Gedeon, T., Goecke, R., Cohn, J. F., & Parker, G. B. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3035535 · Cited by 89
“Filter methods use statistical (e.g., t-score [32], Chi square [33], CFS [34]), similarity (e.g., Fisher score [35], ReliefF [36], Spectral Feature Selection (SPEC) [37]) and information theory approaches (e.g., mRMR [38], Joint Mutual Information (JMI) [39], Conditional Mutual Information…”
MethodA Markov blanket-based method for detecting causal SNPs in GWAS
Han, B., Park, M., & Chen, X.-W. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s3-s5 · Cited by 86
Feature selection via neighborhood multi-granulation fusion
Lin, Y., Li, J., Lin, P.-R., et al. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.05.019 · Cited by 85
“We show the effectiveness of MGF by comparing its performance with the baselines given by SG and Relief.”
MethodInfluentialGaw, N. B., Hawkins-Daarud, A. J., Hu, L., Yoon, H., Wang, L., Xu, Y., Jackson, P., Singleton, K., Baxter, L. C., Eschbacher, J. M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-46296-4 · Cited by 85
“To quantify the contribution of each feature, i.e. the mean image intensities and PI-estimated density, to the prediction made by ML-PI, we developed a modified Relief algorithm 36 , which we call “Relief-ML-PI”.”
MethodStable bagging feature selection on medical data
Alelyani, S. (2021) · Journal Of Big Data · doi:10.1186/s40537-020-00385-8 · Cited by 93
Dessì, N., & Pes, B. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.069 · Cited by 86
“The RF approach can be also adjusted to weight nearest neighbors by their distance (Robnik-Sikonja & Kononenko, 2003).”
BackgroundFeature Selection for Gene Expression Using Model-Based Entropy
Shenghuo Zhu, B., Wang, D., Yu, K. F., Li, T., & Gong, Y. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2008.35 · Cited by 85
“In addition, ReliefF, a widely used feature subset selection method, has also been applied to gene selection [ 22 ].”
BackgroundMethodInfluentialEfficient Semi-Supervised Feature Selection: Constraint, Relevance, and Redundancy
Benabdeslem, K., & Hindawi, M. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.86 · Cited by 85
Performance Assessment of Wind Turbines: Data-Derived Quantitative Metrics
He, Y., & Kusiak, A. (2017) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2017.2715061 · Cited by 84
“It is an instance-based unsupervised leaning heuristic algorithm that identifies parameters that are relevant to the target output [26].”
BackgroundMethodA novel Covid-19 and pneumonia classification method based on F-transform
Tuncer, T., Özyurt, F., Doğan, Ş., & Subaşı, A. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104256 · Cited by 87
Emotionally-Relevant Features for Classification and Regression of Music Lyrics
Malheiro, R., Panda, R., Gomes, P. J. S., & Paiva, R. P. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2598569 · Cited by 92
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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