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Theoretical and Empirical Analysis of ReliefF and RReliefF
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Music Emotion Recognition with Standard and Melodic Audio Features
Panda, R., Rocha, B., & Paiva, R. P. (2015) · Applied Artificial Intelligence · doi:10.1080/08839514.2015.1016389 · Cited by 35
Amarreh, I., Meyerand, M. E., Stafstrom, C. E., Hermann, B. P., & Birn, R. M. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.02.006 · Cited by 35
“Utilizing the feature selection algorithm “ReliefF” (Robnik-Šikonja and Kononenko, 2003) the number of voxels was reduced to the most relevant for classification.”
Image feature selection using genetic programming for figure-ground segmentation
Liang, Y., Zhang, M., & Browne, W. N. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.03.009 · Cited by 35
“Compared with seven FS methods (e.g. ReliefF (Robnik-Šikonja and Kononenko, 2003) and Consist (Dash et al., 2000)), the proposed method is better for 80.18% of the cases.”
MethodInfluentialLe, T. T., Simmons, W. K., Misaki, M., Bodurka, J., White, B. C., Savitz, J. B., & McKinney, B. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx298 · Cited by 35
“…data, and Relief-F has been shown to have high power to identify features that involve complex interactions that are important for distinguishing classes (Draper et al. , 2003; Greene et al. , 2009; Kononenko, 1994; Kononenko et al. , 1997; McKinney et al. , 2013; Sikonja and Kononenko, 2003).”
BackgroundMethodDong, W., Lan, J., Liang, S., et al. (2017) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2017.04.003 · Cited by 35
Restricted-Boltzmann-Based Extreme Learning Machine for Gas Path Fault Diagnosis of Turbofan Engine
Lü, F., Wu, J., Huang, J., et al. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2921032 · Cited by 35
Huang, H., & Liu, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105655 · Cited by 35
Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data
Fu, G., Wu, Y.-J., Zong, M.-J., & Pan, J. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3411-3 · Cited by 35
Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data
Roy, S., Rathee, D., Chowdhury, A., et al. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbd21 · Cited by 35
Oh, T., Kim, D., Lee, S., Won, C. W., Kim, S. Y., Yang, J.-S., Yu, J., Kim, B. S., & Lee, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-06333-1 · Cited by 35
Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets
Camacho-Cogollo, J. E., Bonet, I., Gil, B., & Iadanza, E. (2022) · Electronics · doi:10.3390/electronics11091507 · Cited by 38
“The filter techniques used for feature selection are: information-gain-based methods [73], relief [74], and Gini.”
MethodTaşçı, G., Gün, M. V., Keleş, T., Taşçı, B., Barua, P. D., Taşçı, İ., Doğan, Ş., Bayğın, M., Palmer, E. E., Tuncer, T., et al. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113472 · Cited by 35
Applications of genetic programming in cancer research
Worzel, W. P., Yu, J., Almal, A. A., et al. (2008) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2008.09.025 · Cited by 35
Sparse optimization in feature selection: application in neuroimaging
Kampa, K., Mehta, S., Chou, C., Chaovalitwongse, W. A., & Grabowski, T. J. (2014) · Journal of Global Optimization · doi:10.1007/s10898-013-0134-2 · Cited by 36
“Supervised feature selection algorithms [46,44,52,53] use the statistical dependency between the feature and the class variable to determine the degree of feature relevance.”
MethodHan, B., & Chen, X.-W. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-s2-s9 · Cited by 35
“Since MDR algorithm can not be applied to a large dataset directly, we first reduce the number of SNPs to 10 by ReliefF [19], 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 online modeling for NOx generation prediction in coal-fired boiler
Qiao, J. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.157542 · Cited by 36
Prediction of Cutting Forces in Hard Turning Process Using Machine Learning Methods: A Case Study
Makhfi, S., Dorbane, A., Harrou, F., et al. (2023) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-023-08555-4 · Cited by 34
Nssibi, M., Manita, G., Chhabra, A., Mirjalili, S., & Korbaa, O. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10675-1 · Cited by 34
Fu, Q., Li, Q., Li, X., et al. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120483 · Cited by 34
Fine‐grained traffic classification based on functional separation
Park, B., Won, Y., Chung, J., Kim, M., & Hong, J. W. (2013) · International Journal of Network Management · doi:10.1002/nem.1837 · Cited by 34
“The Relief algorithm has been known as the most successful feature selection method for classi fi cation [64].”
MethodOWA rough set model for forecasting the revenues growth rate of the electronic industry
Liu, J., Cheng, C., Chen, Y., et al. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.06.020 · Cited by 34
“Specifically, it tries to find a good estimate of the following probability to assign the weight for each feature f: wf ¼ Pðdifferent value of f j different classÞ Pðdifferent value of f j same classÞ ð6Þ This approach has shown good performance in various domains (Robnik-Sikonja & Kononenko, 2003).”
BackgroundFeature Selection for Image Classification Based on a New Ranking Criterion
Zhou, X., & Wang, J. (2015) · Journal of Computer and Communications · doi:10.4236/jcc.2015.33013 · Cited by 34
“The reliefF algorithm is a typical example of this type which can effectively provide quality of each feature in problems with dependencies among the feature space [3].”
Selection of effective features for ECG beat recognition based on nonlinear correlations
Chen, Y.-H., & Yu, S. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.09.004 · Cited by 34
O'Dwyer, L., Lamberton, F., Matura, S., Scheibe, M., Miller, J., Rujescu, D., Prvulovic, D., & Hampel, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036024 · Cited by 34
A Cancer Gene Selection Algorithm Based on the K-S Test and CFS
Su, Q., Wang, Y., Jiang, X., Chen, F., & Lu, W. (2017) · BioMed Research International · doi:10.1155/2017/1645619 · Cited by 34
A smartphone-based activity-aware system for music streaming recommendation
Lee, W., Chen, C., Huang, J.-Y., et al. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.06.002 · Cited by 34
Rough set model based feature selection for mixed-type data with feature space decomposition
Kim, K.-J., & Jun, C. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.010 · Cited by 34
Zhang, Y., Gu, T., Luo, C., Kostakos, V., & Seneviratne, A. P. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191788 · Cited by 37
“We employ the Sequential feature selection (SFS) algorithm [3], Random subset feature selection (RSFS) algorithm [49], and ReliefF feature selection algorithm [35] on the feature set.”
MethodBrankovic, A., Hosseini, M., & Piroddi, L. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2833482 · Cited by 35
“These methods are based on individual feature assessment, i.e., they rank the features based on their individual capabilities to discriminate among the classes (see, e.g., [8], [9], [10]).”
MethodAn adaptive heuristic for feature selection based on complementarity
Singha, S., & Shenoy, P. P. (2018) · Machine Learning · doi:10.1007/s10994-018-5728-y · Cited by 34
“For ReliefF, we consider k = 5, m = 250, and exponential decay in weights based on distance [56].”
MethodRelief F-Based Feature Ranking and Feature Selection for Monitoring Induction Motors
Stief, A., Ottewill, J. R., & Baranowski, J. (2018) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2018.8486097 · Cited by 34
“In this analysis k was set to 10, which is the default value proposed in the literature [6].”
BackgroundMethodSemi-Supervised Multi-Label Feature Selection by Preserving Feature-Label Space Consistency
Xu, Y., Wang, J., An, S., et al. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271760 · Cited by 34
“The (cid:12)rst family [1, 26, 31] commonly divides the multi-label learning problem into multiple subproblems, and employs single-label feature evaluation metrics for each sub-problem, such as ReliefF [25], Laplacian score [15], and Information gain [19].”
BackgroundTan, C., Chen, H., Lin, Z., et al. (2018) · Vibrational Spectroscopy · doi:10.1016/j.vibspec.2018.11.004 · Cited by 34
Zheng, X., Zhu, W., Tang, C., & Wang, M. (2019) · Gene · doi:10.1016/j.gene.2019.04.060 · Cited by 34
Yang, P., Wu, T., Yu, M., Chen, F., Wang, C., Yuan, J., Xu, J., & Zhang, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226962 · Cited by 34
“The key idea of the Relief-F algorithm was to estimate the quality of attributes according to how well their values distinguished among instances that were close to each other[26].”
MethodZhou, X., Wang, Q., Zhang, R., et al. (2020) · Minerals Engineering · doi:10.1016/j.mineng.2020.106201 · Cited by 34
Wang, J., Ye, M., Xiong, F., et al. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3056593 · Cited by 34
“In filter-based feature selection methods, criterion function is employed to evaluate the effectiveness of feature subsets based on statistical distance measures or information measures, e.g., information gain (IG) [7], mutual information [8], ReliefF [9], and so on.”
BackgroundMethodSatapathy, S. K., & Loganathan, D. (2021) · Soft Computing · doi:10.1007/s00500-021-06218-x · Cited by 34
Input variable selection for feature extraction in classification problems
Choi, S., Oh, J., Choi, C., et al. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.08.023 · Cited by 34
Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra
Luts, J., Poullet, J., Garcia-Gomez, J. M., et al. (2008) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21626 · Cited by 34
Ariöz, U., Smrke, U., Plohl, N., & Mlakar, I. (2022) · Diagnostics · doi:10.3390/diagnostics12112683 · Cited by 37
“SVM: The SVM technique is one of the most popular methods for depression classi-fication due to having well-fitted binary issues of high dimensionality and suitability for non-linear kernel function settings [75].”
ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
Taş, N. P., Kaya, O., MAÇİN, G., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11092441 · Cited by 33
“ASNet generates six feature vectors (F 1 , F 2 F 6 ) through employing these three CNNs. …F 6 ) through employing these three CNNs To extract more features, we apply three feature selection methods: NCA [32], ReliefF [33], and Chi2 [34], resulting in a total of 18 (= 3 × 6) feature vectors (P 1 , P 2 ...”
BackgroundMethodInfluentialA hybrid multi-objective optimization approach with NSGA-II for feature selection
Vijai, P., & Sivakumar, P. B. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100550 · Cited by 39
Feature selection using structural similarity
Mitra, S., Kundu, P. P., & Pedrycz, W. (2012) · Information Sciences · doi:10.1016/j.ins.2012.02.042 · Cited by 33
“Now complexity of ReliefF algorithm is O(tnNn) where tn is the number training sample used for finding nearest neighbor [33].”
BackgroundDai, H. D., Charnigo, R. J., Becker, M. L., Leeder, J. S., & Motsinger‐Reif, A. A. (2013) · BioData Mining · doi:10.1186/1756-0381-6-1 · Cited by 33
Towards Mining Trapezoidal Data Streams
Zhang, Q., Zhang, P., Long, G., Ding, W., Chengqi Zhang, B., & Wu, X. (2015) · Proceedings · doi:10.1109/icdm.2015.42 · Cited by 32
Emotion recognition with facial expressions and physiological signals
Zhong, B., Qin, Z., Yang, S., Chen, J., Mudrick, N. V., Taub, M., Azevedo, R., & Lobaton, E. J. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285365 · Cited by 37
“3) Feature Selection: The feature selection algorithm called RELIEFF [31], [32] is used to reduce computational complexity and prevent over-fitting, an implementation is available in the MATLAB toolbox: Statistics and Machine Learning.”
MethodAutomatic Bird Vocalization Identification Based on Fusion of Spectral Pattern and Texture Features
Zhang, S.-H., Zhao, Z., Xu, Z., Bellisario, K., & Pijanowski, B. C. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462156 · Cited by 33
“Thus, the ReliefF algorithm [17] is employed for feature ranking and selection, helping to reduce the misclassification rate and computational demands.”
MethodWeighted nearest neighbors feature selection
Bugata, P., & Drotár, P. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.10.004 · Cited by 34
Fractional frequency hybrid model based on EEMD for financial time series forecasting
Li, S., Liu, X., & Lin, A. (2020) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2020.105281 · Cited by 33
A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units
El‐Ganainy, N. O., Balasingham, I., Halvorsen, P. S., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3030031 · Cited by 33
“We investigate three feature selection methods such as Relieff importance ranking method, sequential forward selection method (SFS), and neighborhood component analysis (NCA) [27]–[29].”
MethodBinary Political Optimizer for Feature Selection Using Gene Expression Data
Manita, G., & Korbaa, O. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8896570 · Cited by 33
“*e filter methods (Chi-Square [4], Information Gain [5], Gain Ratio [6], and ReliefF [7]) select a subset of variables by preprocessing the data from a model.”
MethodGreene, C. S., White, B. C., & Moore, J. H. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75286-8_4 · Cited by 33
“This new ReliefF algorithm has been shown to be more robust to noisy attributes and missing data [ 26 ] and is widely used in data mining applications.”
MethodWongseree, W., Assawamakin, A., Piroonratana, T., Sinsomros, S., Limwongse, C., & Chaiyaratana, N. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-294 · Cited by 33
“TuRF exploits the capability of ReliefF by repeatedly executing ReliefF and removing a portion of worst attributes at the end of each execution.”
MethodInfluentialYang, L., Qin, K., Sang, B., et al. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110200 · Cited by 32
Evolutionary Multitask Optimization for Multiform Feature Selection in Classification
Yang, Q.-T., Xu, X.-X., Zhan, Z., Zhong, J., Kwong, S., & Zhang, J. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3535722 · Cited by 34
“2) Parameter Settings: EMTFS is compared with nine state-of-the-art algorithms, including seven EC-based algorithms (CSO [60], HFS-C-P [10], CUS-SPSO [49], MFPSO [61], MTPSO [51], PSO-EMT [50], and MF-CSO [52]) and two traditional filter methods (Relief-F [33] and FCBF [35]).”
Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification
Domingues, I., Amorim, J. P., Abreu, P. H., Duarte, H., & Santos, J. A. M. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489599 · Cited by 32
Kiala, Z., Mutanga, O., Odindi, J., & Peerbhay, K. Y. (2019) · Remote Sensing · doi:10.3390/rs11161892 · Cited by 32
“ReliefF [33] assigns a weight to each feature of a dataset and feature values which are above a predefined threshold are then selected.”
BackgroundFilter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm
Usman, A. M., Yusof, U. K., & Naim, S. (2020) · IEEE Access · doi:10.1109/access.2020.2987057 · Cited by 32
“Reference [24] presented a relief F a variant of relief algorithm for feature ranking which also ranks a score for each feature separately based on the KNN algorithm.”
MethodMulti-Label Feature Selection Based on High-Order Label Correlation Assumption
Zhang, P., Gao, W., Hu, J., et al. (2020) · Entropy · doi:10.3390/e22070797 · Cited by 32
“[42] propose four multi-label feature selection methods based on BR and LP which employ ReliefF (RF) [43] and Information Gain (IG) [44] as the feature evaluation criteria to measure the transformed data (RF-BR, RF-LP, IG-BR and IG-LP).”
MethodA neurodynamic optimization approach to supervised feature selection via fractional programming
Wang, Y., Li, X., & Wang, J. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.01.004 · Cited by 32
Gu, C., Wang, D., Zhang, H., Zhang, J., Zhang, D., & Liang, D. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2020.599886 · Cited by 32
“Researchers have proposed feature selection algorithms for feature screening (Mitra et al., 2002; Marko and Igor, 2003; Valliammal and Geethalakshmi, 2012).”
BackgroundHerman, A., Gruden, K., Blejec, A., Podpečan, V., Motaln, H., Rožman, P. J., Hren, M., Zupančič, K., Veber, M., Verbovšek, U., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125791 · Cited by 32
“Secondly, the Ranker widget ranks microRNAs according to their ability to distinguish between the two sets of samples (control and treatment sets) using the ReliefF algorithm [30].”
MethodXiao, M., & Ju, C. (2022) · Information Sciences · doi:10.1016/j.ins.2022.08.067 · Cited by 32
A novel memetic feature selection algorithm
Montazeri, M., Montazeri, M., Naji, H. R., et al. (2013) · International Conference on Information and Knowledge Technology (IKT) · doi:10.1109/ikt.2013.6620082 · Cited by 32
Evolutionary generation of neural network classifiers—An empirical comparison
Castellani, M. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.07.010 · Cited by 32
“Some authors opted for divide-and-conquer approaches and split the optimisation procedure in different phases.”
BackgroundFeature Selection Using Memetic Algorithms
Yang, C., Chuang, L., Chen, Y.-J., et al. (2008) · doi:10.1109/iccit.2008.81 · Cited by 32
“It estimates the quality of features based on how well their values distinguish between instances that are near to each other [10].”
BackgroundMethodA robust music genre classification approach for global and regional music datasets evaluation
de Sousa, J. M., Pereira, E. T., & Veloso, L. (2016) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2016.7868526 · Cited by 32
Wang, N., Deng, Z., Huang, C., Dong, J. N., Zhu, M.-F., Yao, Z., Chen, A. F., Lü, A., Mi, Q., & Cao, D. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.09.005 · Cited by 32
Glab, K. B., Wehrmeyer, G., Thewes, M., et al. (2024) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2024.105642 · Cited by 31
Machine learning-empowered sleep staging classification using multi-modality signals
Satapathy, S. K., Brahma, B., Panda, B., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02522-2 · Cited by 31
“In this research, artifacts were eliminated by employing a 10th-order Butterworth bandpass filter spanning frequencies from 0.5 to 49.5 Hz [45].”
MethodInfluentialHuang, J., Deng, X., & Hu, L. (2025) · Information Sciences · doi:10.1016/j.ins.2025.121958 · Cited by 31
Jiao, B., Ouyang, Z., Xiao, X., Zhang, C., Xu, T., Yang, Q., Zhu, Y., Liu, Y., Liu, X., Zhou, Y., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103142 · Cited by 32
Correntropy based feature selection using binary projection
Yan, H., Yuan, X., Yan, S., et al. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.04.014 · Cited by 31
Sáez, J. A., Derrac, J., Luengo, J., et al. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.06.012 · Cited by 31
Fast Stability Scanning for Future Grid Scenario Analysis
Liu, R., Verbič, G., Ma, J., et al. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2694048 · Cited by 31
Adaptive feature selection using v-shaped binary particle swarm optimization
Teng, X., Dong, H., & Zhou, X. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173907 · Cited by 31
Moradi, M., Chaibakhsh, A., & Ramezani, A. (2018) · Applied Mathematical Modelling · doi:10.1016/j.apm.2018.03.002 · Cited by 31
Tang, T., Bo, L., Liu, X., et al. (2018) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2018.03.032 · Cited by 31
Binary Differential Evolution Based on Individual Entropy for Feature Subset Optimization
Li, T., Dong, H., & Sun, J. (2019) · IEEE Access · doi:10.1109/access.2019.2900078 · Cited by 31
“1) The importance of feature is measured by its relevance to the class label in the ReliefF [30].”
BackgroundInfluentialA Hybrid Feature Selection Approach for Handling a High-Dimensional Data
Venkatesh, B., & Anuradha, J. (2019) · Lecture notes in networks and systems · doi:10.1007/978-981-13-7082-3_42 · Cited by 31
Fuzzy kernel feature selection with multi-objective differential evolution algorithm
Hançer, E. (2019) · Connection Science · doi:10.1080/09540091.2019.1639624 · Cited by 31
“We recently introduced two DE-based filter approaches (Hancer et al., 2017; Hancer, Xue, & Zhang, 2018): the former one combined mutual relevance and ReliefF as the objective function on the DE framework, and the latter one designed an objective function using the principles of ReliefF, Fisher Score and mutual relevan…”
MethodChen, L. (2015) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2015.03.002 · Cited by 31
Electricity load forecasting based on autocorrelation analysis
Sood, R., Koprinska, I., & Agelidis, V. G. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596877 · Cited by 31
“RRelietF [ 15 ] is an extension of RelietF for regression problems.”
MethodA novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography
Liparulo, L., Zhang, Z., Panella, M., Gu, X., & Fang, Q. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1597-3 · Cited by 31
“Different evaluation methods including InfoGain, ReliefF and Pearson correlation coefficient are adopted in order to analyze the contribution, correlation strength and statistical significance of the selected features.”
BackgroundInfluentialWang, S., Li, H., Zhang, Y. J., et al. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/4164296 · Cited by 31
Attribute Interactions in Machine Learning
Jakulin, A. (2003) · Repository of the University of Ljubljana (University of Ljubljana) · Cited by 30
Chen, K., Tsai, C., Hu, Y., & Hu, C.-W. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114145 · Cited by 35
Feature Selection for Social Media Data
Tang, J., & Liu, H. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2629587 · Cited by 30
“Sikonja and Kononenko 2003], mRmR [Peng et al. 2005], and LaplacianScore [He et al. 2006] and its extensions [Zhao and Liu 2007].”
BackgroundPigmented Skin Lesions Classification Using Dermatoscopic Images
Capdehourat, G., Corez, A., Bazzano, A., & Musé, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10268-4_63 · Cited by 30
“Feature selection is performed using ReliefF [8] and CFS algorithms [9].”
MethodRecognition and assessment of different factors which affect flicker in wind turbines
Fooladi, M., & Foroud, A. A. (2015) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2014.0419 · Cited by 30
Key Considerations and Methods in the Study of Gene–Environment Interactions
Simon, P. H. G., Sylvestre, M., Tremblay, J., & Hamet, P. (2016) · American Journal of Hypertension · doi:10.1093/ajh/hpw021 · Cited by 32
Machine Learning Techniques applied in risk assessment related to food safety
IZSTO, UBESP, DSCB, ZETA, Ru, G., Crescio, M. I., Ingravalle, F., Maurella, C., Gregori, D., Lanera, C., et al. (2017) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2017.en-1254 · Cited by 32
Investigating the contribution of distance-based features to automatic sleep stage classification
Gharbali, A. A., Najdi, S., & Fonseca, J. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.03.001 · Cited by 30
Classification of bird species from video using appearance and motion features
Atanbori, J., Duan, W., Shaw, E., Appiah, K., & Dickinson, P. (2018) · Ecological Informatics · doi:10.1016/j.ecoinf.2018.07.005 · Cited by 33
Using Support Vector Machines and neural networks to classify Merlot wines from South America
da Costa, N. L., Llobodanin, L. A. G., de Castro, I. A., & Barbosa, R. (2018) · Information Processing in Agriculture · doi:10.1016/j.inpa.2018.10.003 · Cited by 31
Robust unsupervised feature selection by nonnegative sparse subspace learning
Zheng, W., Yan, H., & Yang, J. J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.015 · Cited by 30
Wijaya, D. R., & Afianti, F. (2020) · IEEE Access · doi:10.1109/access.2020.2974982 · Cited by 30
An evidential reasoning rule based feature selection for improving trauma outcome prediction
Almaghrabi, F., Xu, D., & Yang, J. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107112 · Cited by 30
“The ER rule is compared to two common FS techniques: a filter FS technique, which is the ReliefF algorithm [16] and variables’ importance weight extracted from the RF algorithm [15], which is an embedded FS technique.”
MethodTowards Feature-Based Performance Regression Using Trajectory Data
Jankovič, A., Eftimov, T., & Doerr, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72699-7_38 · Cited by 30
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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