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Theoretical and Empirical Analysis of ReliefF and RReliefF
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“[28] applied Random Forest (RF) [35] and RReliefF [36] to evaluate a number of features, with respect to their ability to predict the short-term subcutaneous glucose concent rations.”
MethodDetecting gene–gene interactions that underlie human diseases
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“Also if several features are involved in one way of the explanation, all of them get the credit in their quality estimate (Robnik-Šikonja and Kononenko, 2003).”
BackgroundMethodResultInfluentialA review of feature selection methods with applications
Jović, A., Brkić, K., & Bogunovic, N. (2015) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2015.7160458 · Cited by 1,238
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“weights of each feature depending on their ability to discriminate between two classes [51].”
BackgroundEpistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans
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“We show that two powerful feature selection algorithms, ReliefF ( Robnik-Sikonja & Kononenko, 2003 ) and Laplacian Score (He et al., 2005) are special cases of the proposed framework.”
MethodInfluentialFeature Selection: A Data PerspectivePreprint
Li, J., Cheng, K., Wang, S., Morstatter, F., Treviño, R. P., Tang, J., & Liu, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07996 · Cited by 891
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“As previously shown in classification problems [17], this number is context-dependent, however the choice of the most parsimonious model seems reasonable as we do not expect many correlations among variables.”
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An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
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“Filter methods (Chi-Square [3], Information Gain [4], Gain Ratio [5], ReliefF [6]) rely on some data properties without involving a specific learning algorithm.”
MethodDevelopment of spectral indices for detecting and identifying plant diseases
Mahlein, A., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke, E.-C. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.09.019 · Cited by 665
Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application
Cantor, R. M., Lange, K. L., & Sinsheimer, J. S. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.11.017 · Cited by 638
Stability of feature selection algorithms: a study on high-dimensional spaces
Kalousis, A., Prados, J., & Hilario, M. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0040-8 · Cited by 642
Moore, J. H., Gilbert, J. C., Tsai, C., Chiang, F., Holden, T. M., Barney, N., & White, B. C. (2006) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.11.036 · Cited by 607
“Other algorithms and metrics such as ReliefF (reviewed by Robnik-Siknja and Kononenko, 2003) could be used to select interesting attributes.”
MethodInfluentialFeature subset selection in large dimensionality domains
Gheyas, I. A., & Smith, L. S. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.06.009 · Cited by 597
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
Hou, C., Nie, F., Li, X., Yi, D., & Wu, Y. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2272642 · Cited by 555
“Besides, we also compare one of the famous supervised feature selection approach RelieF in this scenario [45].”
ResultBioinformatics challenges for genome-wide association studies
Moore, J. H., Asselbergs, F. W., & Williams, S. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp713 · Cited by 540
A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
Čeperić, E., Čeperić, V., & Barić, A. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2269803 · Cited by 491
“, feature selection based on genetic algorithms and information theory (GA&IT) [44], feature selection using mutual information and maximum-relevance minimum-redundancy criterion (MRMR) [45], feature selection using ReliefF algorithm [46], sequential feature selection with stepwise regression (SteepwiseFS) [47].”
MethodA Regression Approach to Music Emotion Recognition
Yang, Y., Lin, Y., Su, Y.-F., & Chen, H. H. (2008) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2007.911513 · Cited by 463
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
Zhu, Z., Ong, Y.-S., & Dash, M. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2006.883267 · Cited by 446
“Therefore, it makes good sense that features with high score in ReliefF are more likely to help 1NN identify the correct nearest neighbor, hence generating good classification accuracy.”
InfluentialFilter Methods for Feature Selection – A Comparative Study
Sánchez‐Maroño, N., Alonso‐Betanzos, A., & Tombilla-Sanromán, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-77226-2_19 · Cited by 427
“6 (??) 100 200 150 4,6,8,11,17,19 [1-11],[13-20],38,44,100, 112,120,123,144,172,180,185,191,197,200”
InfluentialMafarja, M. M., Aljarah, I., Heidari, A. A., Hammouri, A. I., Faris, H., Al-Zoubi, A. M., & Mirjalili, S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.12.037 · Cited by 419
A survey on swarm intelligence approaches to feature selection in data mining
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Differential evolution for filter feature selection based on information theory and feature ranking
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“To achieve this goal, a novel filter evaluation criterion (named MIRFFS) based on the concepts of mutual relevance, RelifF [27] and Fisher Score [28] is proposed, and using this proposed criterion, the standard DE and multi-objective DE (MODE) based feature selection approaches are developed.”
BackgroundMethodA feature selection model based on genetic rank aggregation for text sentiment classification
Onan, A., & Korukoğlu, S. (2015) · Journal of Information Science · doi:10.1177/0165551515613226 · Cited by 385
“Hence, it can handle properly with the attributes when there is a strong dependency between them [30].”
BackgroundExploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
Wu, W., Parmar, C., Großmann, P., Quackenbush, J. F., Lambin, P., Bussink, J., Mak, R. H., & Aerts, H. J. W. L. (2016) · Frontiers in Oncology · doi:10.3389/fonc.2016.00071 · Cited by 378
Behmann, J., Mahlein, A., Rumpf, T., Römer, C., & Plümer, L. (2014) · Precision Agriculture · doi:10.1007/s11119-014-9372-7 · Cited by 404
“Filter methods use inherent characteristics of features to estimate a feature ranking according to their relevance (Robnik-Šikonja & Kononenko 2003).”
MethodFeature selection with dynamic mutual information
Liu, H., Sun, J., Liu, L., et al. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.10.028 · Cited by 373
Feature Selection for Clustering: A Review
Alelyani, S., Tang, J., & Liu, H. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315373515-2 · Cited by 366
Trace ratio criterion for feature selection
Nie, F., Xiang, S., Jia, Y., et al. (2008) · Cited by 360
Feature Selection: An Ever Evolving Frontier in Data Mining
Liu, H., Motoda, H., Setiono, R., & Zhao, Z. (2010) · Cited by 353
Song, X., Yon, Z., Gong, D., et al. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3061152 · Cited by 349
“1) ReliefF FS (ReliefF) [60].”
Epistasis and Its Implications for Personal Genetics
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Deep feature weighting for naive Bayes and its application to text classification
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“Thus, they proposed a gain ratio-based feature weighting approach (GRFW) that assigns weights to features proportional to their gain ratio score: Wi ¼ GainRatioðAiÞ mPm i ¼ 1 GainRatioðAiÞ ; ð6Þ wherem is the number of features, GainRatioðAiÞ is the information gain ratio of using the feature Ai to partition the train…”
BackgroundMethodInfluentialA Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone
Wang, A., Chen, G., Yang, J., Zhao, S., & Chang, C.-Y. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2545708 · Cited by 324
“Generally, feature selection methods can be categorized into two groups: filter-based and wrapper-based methods [39], [45].”
MethodTutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
On Similarity Preserving Feature Selection
Zhao, Z., Wang, L., Liu, H., et al. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.222 · Cited by 312
Semi-supervised Feature Selection via Spectral Analysis
Zhao, Z., & Liu, H. (2007) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972771.75 · Cited by 305
Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection
Li, Z., Liu, J., Yang, Y., et al. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.65 · Cited by 303
Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.13649 · Cited by 300
“Some examples of supervised feature selection methods are kernel feature selection 14 , Relief 15 , and generalized Fisher score 16 .”
MethodWiFi-ID: Human Identification Using WiFi Signal
Zhang, J., Wei, B. W., Hu, W. S., & Kanhere, S. S. (2016) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss.2016.30 · Cited by 290
Efficient Spectral Feature Selection with Minimum Redundancy
Zhao, Z., Wang, L., & Liu, H. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7671 · Cited by 294
Relevance assessment of full-waveform lidar data for urban area classification
Mallet, C., Frédéric Bretar, A., Roux, M., Soergel, U., & Heipke, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.09.008 · Cited by 292
“Two techniques are investigated: – ReliefF uses the K nearest neighbors of each instance per class to calculate a weight for each feature (Robnik-Šikonja and Kononenko, 2003).”
MethodFeature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Tuv, E., Borisov, A., Runger, G. C., & Torkkola, K. (2009) · doi:10.5555/1577069.1755828 · Cited by 280
“Several common filter methods such as ReliefF (Robnik-Sikonja and Kononenko, 2003), CFS (Hall, 2000), and FOCUS (Almuallin and Dietterich, 1994) were modified with sequential search andcompared by Yu and Liu (2004).”
MethodFeature Selection Based on Neighborhood Self-Information
Wang, C., Huang, Y., Shao, M., Hu, Q., & Chen, D. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2923430 · Cited by 273
“Presently, many feature evaluation functions have been developed, such as consistency and correlation [12], [13]; dependency degree [14], [15]; distance metrics [9], [16]; information gain [17]–[20]; classifier error rate [3], [21]; and so on.”
BackgroundToward optimal feature selection using ranking methods and classification algorithms
Novakovic, J., Štrbac, P., & Bulatović, D. (2011) · Yugoslav journal of operations research · doi:10.2298/yjor1101119n · Cited by 269
A Fitting Model for Feature Selection With Fuzzy Rough Sets
Wang, C., Qi, Y., Shao, M., Hu, Q., Chen, D., Qian, Y., & Lin, Y. (2016) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2574918 · Cited by 267
“Distance [26], [45], correlation [11], consistency [6], and mutual information [25], [37], [48] are usually regarded as being feasible feature evaluation functions.”
BackgroundGollou, A. R., & Ghadimi, N. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-152073 · Cited by 267
“RF [27] is an instance-based feature ranking method from the Relief family of methods for feature selection applicable to both classification and forecasting tasks.”
MethodSelecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization
Hu, Q., Witold Pedrycz, C., Yu, D., & Lang, J. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2024166 · Cited by 263
“The ReliefF algorithm [39] relies on this idea.”
BackgroundMethodInfluentialAn Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification
Chen, K., Xue, B., Zhang, M., & Zhou, F. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3042243 · Cited by 269
“In addition, we also compare the proposed PSO-EMT method with five classical FS algorithms, which are: 1) CFS (statistical-based) [44]; 2) FCBF (information theoretical-based) [45]; 3) ReliefF (similarity-based) [39]; 4) SBMLR (sparsity-based) [46]; and 5) SPEC (graph theory-based) [47].”
MethodGlobal and Local Structure Preservation for Feature Selection
Liu, X., Wang, L., Zhang, J., Yin, J., & Liu, H. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2287275 · Cited by 256
“The local geometric structure can be captured by building a graph, where each sample is treated as a node, and an edge is put between two samples if they are neighbors.”
BackgroundMachine Learning for Detecting Gene-Gene Interactions
McKinney, B. A., Reif, D. M., Ritchie, M. D., & Moore, J. H. (2006) · Applied Bioinformatics · doi:10.2165/00822942-200605020-00002 · Cited by 257
“The key here is to identify filter strategies such as ReliefF [87] that are capable of assigning a high quality or relevance to SNPs that might be involved in a nonlinear interaction.”
BackgroundBenchmarking relief-based feature selection methods for bioinformatics data mining
Urbanowicz, R. J., Olson, R. S., Schmitt, P., Meeker, M., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.015 · Cited by 290
“This unique ability has been attributed to Relief’s use of ‘nearest neighbor instances’ in calculating feature weights (Kononenko et al., 1997; Kononenko and ˇSikonja, 2008).”
BackgroundMethodInfluentialvan Zyl, C., Ye, X., & Naidoo, R. M. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122079 · Cited by 297
Redundancy based feature selection for microarray data
Lei, Y., & Liu, H. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014149 · Cited by 250
“A subset of features is selected from top of the ranking list [ 18 ].”
BackgroundAli, S. A., Parvin, F., Vojteková, J., et al. (2020) · Geoscience Frontiers · doi:10.1016/j.gsf.2020.09.004 · Cited by 249
Reducing Features to Improve Code Change-Based Bug Prediction
Shivaji, S., Whitehead, E. J., Akella, R., & Kim, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.43 · Cited by 245
“Relief-F attribute selection—Relief-F in an extension to the Relief algorithm [31].”
MethodKotsiantis, S. B. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9234-x · Cited by 247
“The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets (Platt 1999).”
MethodRobnik‐Šikonja, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30115-8_34 · Cited by 246
“Details of the algorithm and its analysis can be found in [13].”
BackgroundSelf-citationMa, L., Fu, T., Blaschke, T., Li, M., Dirk Tiede, A., Zhou, Z., Ma, X., & Chen, D. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6020051 · Cited by 247
“The Relief-F method uses training instances randomly sampled from the data with attribute values and the class value to calculate the weight vector w representing the quality of all features [33].”
MethodTechniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review
Pathan, S., Prabhu, K. G., & Siddalingaswamy, P. C. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.07.010 · Cited by 243
Searching for interacting features
Zhao, Z., & Liu, H. (2007) · International Joint Conference on Artificial Intelligence · Cited by 235
Kernelized Fuzzy Rough Sets and Their Applications
Hu, Q., Yu, D., Witold Pedrycz, C., & Chen, D. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.260 · Cited by 238
“Moreover, generalized dependency and classification certainty are better than or equivalent to Relief and ReliefF algorithms in both cases of k ¼ 1 and k ¼ 5.”
BackgroundMethodInfluentialExploring feature selection and classification methods for predicting heart disease
Spencer, R., Thabtah, F., Abdelhamid, N., & Thompson, M. (2020) · Digital Health · doi:10.1177/2055207620914777 · Cited by 263
Unsupervised Feature Selection with Adaptive Structure Learning
Du, L., & Shen, Y.-D. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783345 · Cited by 232
“Clearly, both the knn graph and the graph Laplacian are determined by all the relevant and irrelevant features.”
BackgroundFeature reduction for hepatocellular carcinoma prediction using machine learning algorithms
Mostafa, G., Mahmoud, H. A., El‐Hafeez, T. A., & ElAraby, M. E. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00944-3 · Cited by 232
Li, S., Wang, P., & Goel, L. (2015) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2015.2438322 · Cited by 230
“The significant improvements over CA, MI, and RReliefF have shown that relevance analysis alone is not sufficient for feature selection.”
MethodGaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
Hu, Q., Zhang, L., Chen, D., Witold Pedrycz, C., & Yu, D. (2010) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2010.01.004 · Cited by 227
“Features 7, 12 and 13 are the three best features with respect to entropy and ReliefF.”
MethodInfluentialLi, Y., Xu, M., Wang, R., & Huang, W. (2015) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2015.09.016 · Cited by 227
A random forest classifier for lymph diseases
Azar, A. T., Elshazly, H. I., Hassanien, A. E., & ElKorany, A. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.11.004 · Cited by 232
A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems
Xu, Y. H., Dong, Z. Y., Zhao, J. H., Zhang, P., & Wong, K. P. (2012) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2183899 · Cited by 222
“In this paper, a distance-based algorithm called RELIEF [23], [24] is introduced and applied for single feature estimation and selection.”
BackgroundMethodEnsemble feature selection for high-dimensional data: a stability analysis across multiple domains
Pes, B. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04082-3 · Cited by 241
Xiao, R., Hu, Q., & Li, J. (2019) · Measurement · doi:10.1016/j.measurement.2019.06.050 · Cited by 220
Generalized Fisher score for feature selection
Gu, Q., Li, Z., & Han, J. (2011) · Cited by 220
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Spolaôr, N., Cherman, E. A., Monard, M. C., et al. (2013) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2013.02.010 · Cited by 219
“The basic idea of ReliefF is to reward an attribute for having different values on a pair of similar examples from different classes, and penalize it for having different values on examples from the same class [6,17].”
BackgroundChen, K., Xue, B., Zhang, M., & Zhou, F. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3100056 · Cited by 223
New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases
Huang, W., Guan, Q., Luo, J., et al. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2294961 · Cited by 211
“To find the best combination of single wavelength and wavelength difference, the RELIEF-F algorithm [20] was used.”
MethodArora, N., & Kaur, P. D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105936 · Cited by 226
Gou, B., Xu, Y. H., Xia, Y., Deng, Q., & Ge, X. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.2994351 · Cited by 211
“equation to solve the multiclassification problem [35]:”
MethodQuadratic Programming Feature Selection
Rodríguez-Luján, I., Huerta, R., Elkan, C., Cruz, C. S., & Ungar, L. (2010) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 207
Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study
Dubey, R., Zhou, J., Wang, Y., Thompson, P. M., & Ye, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.005 · Cited by 212
“The Relief-F algorithm improves the Relief algorithmby introducing k-nearest neighbors from each class (Robnik-Šikonja and Kononenko, 2003).”
Correlation and instance based feature selection for electricity load forecasting
Koprinska, I., Rana, M., & Agelidis, V. G. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.02.017 · Cited by 203
Accelerating wrapper-based feature selection with K-nearest-neighbor
Wang, A., An, N., Chen, G., Li, L., & Alterovitz, G. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.03.009 · Cited by 203
“In our study, we used ReliefF, which is a distance-based filter measure that has great power in selecting discriminative features and good stability toward the perturbation of the training set [5,40], to generate a ranked feature set from the original feature space.”
MethodChapman & Hall/CRC Data Mining and Knowledge Discovery Series
Hristidis, V. (2010) · Chapman & Hall/CRC data mining and knowledge discovery series · doi:10.1201/chdaminodis · Cited by 203
Gene selection algorithm by combining reliefF and mRMR
Zhang, Y., Ding, C. H. Q., & Li, T. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-s2-s27 · Cited by 200
“Detailed discussions on ReliefF can be found in [ 10 ] and recently, it was shown that ReliefF is an on-line solution to a convex optimization problem, maximizing a marginbased algorithm [27].”
BackgroundMethodInfluentialA novel feature selection method considering feature interaction
Zeng, Z., Zhang, H., Zhang, R., & Yin, C. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.02.025 · Cited by 197
Wang, Y., Makedon, F. S., Ford, J., & Pearlman, J. D. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti192 · Cited by 195
“This approach has shown good performance in various domains (Robnik-Sikonja and Kononenko, 2003).”
BackgroundReyes, Ó., Morell, C., & Ventura, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.045 · Cited by 194
“The mathematical interpretation of the Relief scheme explains the success of this family of algorithms in several real applications [19, 21, 23, 24, 39–42].”
BackgroundMethodInfluentialNag, K., & Pal, N. R. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2404806 · Cited by 179
“CFS [85], (4) ReliefF [86], (5) Consist [87], and (6) FOCUSSF [88].”
MethodAn improved Dragonfly Algorithm for feature selection
Hammouri, A. I., Mafarja, M. M., Al‐Betar, M. A., Awadallah, M. A., & Doush, I. A. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106131 · Cited by 198
Knowledge discovery in metabolomics: An overview of MS data handling
Boccard, J., Veuthey, J., & Rudaz, S. L. (2010) · Journal of Separation Science · doi:10.1002/jssc.200900609 · Cited by 204
Integration of graph clustering with ant colony optimization for feature selection
Moradi, P., & Rostami, M. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.04.007 · Cited by 188
“ReliefF [49] is an extension of the Relief algorithm, which applies a feature weighting scheme and searches for several nearest neighbors.”
BackgroundMethodReliefF for Multi-label Feature Selection
Spolaôr, N., Cherman, E. A., Monard, M. C., et al. (2013) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2013.10 · Cited by 186
“In this work, we propose an extension of the single-label ReliefF algorithm [6] for multi-label feature selection, named RF-ML , which deals with multi-label data directly, i.e. , without any data transformation.”
BackgroundMethodInfluentialLiu, X., & Tang, J. (2013) · IEEE Systems Journal · doi:10.1109/jsyst.2013.2286539 · Cited by 185
“For the feature selection methods, we investigate the F-score [23], Relief [24], SVM-RFE [17], SVM-RFE with a minimum redundancy–maximum relevance (mRMR) filter [SVM-RFE (mRMR)] methods [19], and the proposed SVM-RFE with an NMI feature selection (NMIFS) filter (SRN) method.”
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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