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Theoretical and Empirical Analysis of ReliefF and RReliefF
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Arifeen, S. U., Amin, M. N., Ahmad, W., Althoey, F., Ali, M., Alotaibi, B. S., & Abuhussain, M. A. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.133485 · Cited by 26
Extended Relief Algorithms in Instance-Based Feature Filtering
Park, H., & Kwon, H. (2007) · International Conference on Advanced Language Processing and Web Information Technology · doi:10.1109/alpit.2007.16 · Cited by 26
Implementing ReliefF filters to extract meaningful features from genetic lifetime datasets
Beretta, L., & Santaniello, A. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2010.12.003 · Cited by 26
“One of the most successful feature selection algorithm is Relief [15] and its ReliefF extension [16], which is capable of evaluating an attribute’s importance considering dependence from other attributes, appreciating thus their synergistic effect [17].”
BackgroundEnsemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Noghani, F. F., & Moattar, M. H. (2017) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22044/jadm.2016.788 · Cited by 26
“The second term is to ensure that the contribution of each class is in the range of [0, 1] and sums to 1 [35].”
BackgroundInfluentialProcess-Monitoring-for-Quality — Big Models
Escobar, C. A., Abell, J. A., Hernández-de-Menéndez, M., & Morales-Menéndez, R. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.07.153 · Cited by 26
Song, W., Wang, H., Maguire, P. D., & Nibouche, O. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2018.08.011 · Cited by 25
Golf swing classification with multiple deep convolutional neural networks
Jiao, L., Bie, R., Wu, H., Wei, Y., Ma, J., Umek, A., & Kos, A. (2018) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147718802186 · Cited by 27
EMG Feature Selection for Diagnosis of Neuromuscular Disorders
Benazzouz, A., Guilal, R., Amirouche, F. M. L., et al. (2019) · International Conference on Networking and Advanced Systems (ICNAS) · doi:10.1109/icnas.2019.8807862 · Cited by 26
“The k nearest neighbors of the same class are named Near Hit and the k nearest neighbors of the other ones are named Near Misses [20].”
BackgroundAnalysis of region of interest (RoI) of brain for detection of depression using EEG signal
Mahato, S., & Paul, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15827-7 · Cited by 28
Ma, H., Li, M., Lv, S., et al. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111948 · Cited by 25
SegMine workflows for semantic microarray data analysis in Orange4WS
Podpečan, V., Nada Lavrač, A., Mozetič, I., Novak, P. K., Igor Trajkovski, A., Langohr, L., Kulovesi, K., Toivonen, H. T. T., Petek, M., Motaln, H., et al. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-416 · Cited by 25
“Second, the genes are ranked using the ReliefF [32] algorithm or Student’s t-test.”
MethodBird sounds classification by large scale acoustic features and extreme learning machine
Qian, K., Zhang, Z., Ringeval, F., & Schuller, B. W. (2015) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2015.7418412 · Cited by 25
“…p-centre [21] is used for detection of ‘syllables’ from bird sound audio recordings; 2) the openSMILE toolkit [22] is used to extract a large acoustic feature set; 3) Extreme Learning Machines (ELM) [23] with the ReliefF algorithm [24] are introduced for machine learning to classify bird sounds.”
MethodSupervised feature selection via matrix factorization based on singular value decomposition
Zare, M., Eftekhari, M., & Aghamollaei, G. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.01.003 · Cited by 25
PR-KELM: Icing level prediction for transmission lines in smart grid
Chen, Y., Fan, J., Deng, Z., et al. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.002 · Cited by 25
Blood pressure prediction from speech recordings
Ankışhan, H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101842 · Cited by 25
Practical Challenges and Recommendations of Filter Methods for Feature Selection
Rajab, M. A., & Wang, D. Y. Q. (2020) · Journal of Information & Knowledge Management · doi:10.1142/s0219649220400195 · Cited by 25
“These authors focus on selecting features based on their true rankings obtained by applying ReliefF (Robnik-Šikonja and Kononenko, 2003) and Fisher Score (Bishop, 1995) rather than focusing on their mutual redundancies.”
MethodInfluentialZhang, C., Kong, L., Xu, Q., Zhou, K., & Pan, H. (2020) · Measurement Science and Technology · doi:10.1088/1361-6501/aba93b · Cited by 25
“According to the experiments in section 3.3.5, JMIM is still compared with ReliefF, JMI and mRMR, and the results are shown in figure 13.”
MethodInfluentialA predictive noise correction methodology for manufacturing process datasets
Oleghe, O. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00367-w · Cited by 25
Arabameri, A., Santosh, M., Rezaie, F., et al. (2021) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-021-02036-7 · Cited by 25
“Numerous techniques can be used to discern the contribution a factor makes to a modelling outcome, these include fuzzy-rough sets, IGR, relief-f, and others (Robnik-Šikonja and Kononenko 2003; Mardani et al. 2017).”
MethodNtakolia, C., Priftis, D., Charakopoulou-Travlou, M., Rannou, I., Magklara, K., Giannopoulou, I., Kotsis, K. T., Serdari, A., Tsalamanios, E., Grigoriadou, A., et al. (2022) · Healthcare · doi:10.3390/healthcare10010149 · Cited by 25
Quality Assessment of Components of Wheat Seed Using Different Classifications Models
Fazel-Niari, Z., Afkari-Sayyah, A. H., Abbaspour‐Gilandeh, Y., et al. (2022) · Applied Sciences · doi:10.3390/app12094133 · Cited by 25
“Among different methods for reducing features, the ReliefF algorithm works well for correlative and noisy features, and it can calculate the correlation between features and the related groups [17].”
MethodFast, accurate and explainable time series classification through randomization
Cabello, N., Naghizade, E., Qi, J., & Kulik, L. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00978-w · Cited by 32
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach
Mendes‐Moreira, J., Jorge, A., Soares, C., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03070-3_15 · Cited by 25
“For task 1, we have introduced three different methods (CART-ln, knn-kdt and knn-RRF) in order to test whether selection methods that take into account the value of the target in the training examples yields better results in comparison to methods which are only based on the independent variables (knn with Euclidean d…”
MethodMemetic Algorithms for Feature Selection on Microarray Data
Zhu, Z., & Ong, Y.-S. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72383-7_155 · Cited by 25
“In this study the ReliefF [14] is considered.”
MethodMeng, J., Zhang, J., & Luan, Y. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2361329 · Cited by 25
CBFS: High Performance Feature Selection Algorithm Based on Feature Clearness
Seo, M., & Oh, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040419 · Cited by 25
“FeaLect, FSDD, and Relief feature selection algorithms are compared with proposed CBFSorg, CBFSintersection, and CBFSexact.”
MethodInfluentialComputerized system for recognition of autism on the basis of gene expression microarray data
Latkowski, T., & Osowski, S. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.11.004 · Cited by 25
Su, H., Xing, F., Lee, J. D., Peterson, C. A., & Yang, L. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.151 · Cited by 25
“like Laplacian score [57], trace ratio [58], relief and reliefF [59], and SPEC [60], and HSIC [61], evaluate features individually and thus are not specifically designed to handle”
Neuro-Fuzzy Physiological Computing to Assess Stress Levels in Virtual Reality Therapy
Tartarisco, G., Carbonaro, N., Tonacci, A., Bernava, G. M., Arnao, A. A., Crifaci, G., Cipresso, P., Riva, G., Gaggioli, A., Rossi, D. D., et al. (2015) · Interacting with Computers · doi:10.1093/iwc/iwv010 · Cited by 26
Network properties of decoys and CASP predicted models: a comparison with native protein structures
Chatterjee, S., Ghosh, S., & Vishveshwara, S. (2013) · Molecular BioSystems · doi:10.1039/c3mb70157c · Cited by 25
Acharya, U. R., Ng, W. L., Rahmat, K., et al. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.11.004 · Cited by 25
“The averages of their contributions are dded to Relief’s estimate [100].”
Kowalski, P. A., & Kusy, M. (2017) · Computational Intelligence · doi:10.1111/coin.12149 · Cited by 25
“According to the theoretical analysis in the work of Robnik-Šikonja and Kononenko, 25 when we take a greater number of near neighbors, we reduce the risk that there are no nearest neighbors with differences in values of a certain nominal attribute.”
MethodSánchez, R., Lucero, P., Macancela, J.-C., Cerrada, M., Vásquez, R. E., & Pacheco, F. (2017) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc.2017.29 · Cited by 25
“It is an update of ReliefF [28] and its process starts by randomly selecting an instance , then looks for k of its closest neighbors of the same class, called nearest hit , and k nearest neighbors of different classes, called nearest misses .”
Li, Z., Tan, J., Li, S., et al. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.11.020 · Cited by 25
Aljarah, I., Faris, H., Heidari, A. A., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3097206 · Cited by 25
Kukar, M., Kononenko, I., & Grošelj, C. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.04.009 · Cited by 25
Self-citationA Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR
Zhang, Y., Ding, C. H. Q., & Li, T. (2007) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2007.4375560 · Cited by 25
Gandotra, S., Kumar, Y., Modi, N., Choi, J., Shafi, J., & Ijaz, M. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10872-6 · Cited by 24
“A machine learning method is being used by Sun et al. (2017) to analyse Pap smear cervical cell pictures, with a random forest classifier with ReliefF (Robnik-Šikonja and Kononenko 2003) feature selection being applied.”
Feature selection for regression problems based on the Morisita estimator of intrinsic dimension
Golay, J., Leuenberger, M., & Kanevski, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.008 · Cited by 25
“In Section 6, real world applications from publicly accessible repositories are presented, and a comparison with a benchmark algorithm, RReliefF [6], is carried out using Extreme Learning Machine (ELM) [27].”
MethodInfluentialMass Spectrometry Metabolomic Data Handling for Biomarker Discovery
Boccard, J., & Rudaz, S. L. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-394446-7.00027-3 · Cited by 24
Robust, efficient and privacy-preserving violent activity recognition in videos
Imran, J., Raman, B., & Rajput, A. S. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373942 · Cited by 24
“These templates are used to extract features via AlexNet model, followed by dimensionality reduction using Relieff [32] feature selection method.”
MethodA Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
Pham, B. T., Nguyen, M. D., Al-Ansari, N. A., Tran, Q. A., Ho, L. S., Le, H. V., & Prakash, I. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/7631493 · Cited by 26
Meniscal Tear and ACL Injury Detection Model Based on AlexNet and Iterative ReliefF
Key, S., Baygin, M., Demir, S., et al. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00581-3 · Cited by 24
“Step 4: Feature selection via RelifF algorithm by using Algorithm 3 (2) WR � fti � = WR � fti � − ∑k j=1 dist(A, T ,N) n × k + ∑ C≠class(R) � Pr(C) 1−Pr(R) × ∑k l=1 dist(A, T ,M) � n × k (3)dist ( A, L1, L2 ) = { 0, L1 = L2 1, L1 ≠ L2 (4)dist ( A, L1, L2 ) = ||L1 − L2|| Amax − Amin 1 3 Input: Selected features (sf) an…”
MethodInfluentialWang, Y., & Makedon, F. S. (2004) · IEEE Computational Systems Bioinformatics Conference (CSB) · doi:10.1109/csb.2004.1332474 · Cited by 24
“wf = P (different value of f |different class) −P (different value of f |same class) This approach has shown good performance in various domains [6].”
BackgroundGarcía‐Jacas, C. R., García‐González, L. A., Martínez-Rios, F., Tapia-Contreras, I. P., & Brizuela, C. A. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac428 · Cited by 25
Yu, K., Sun, S., Liang, J., et al. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119619 · Cited by 24
A new filter-based gene selection approach in the DNA microarray domain
Ouaderhman, T., Chamlal, H., & Janane, F. Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122504 · Cited by 24
Zhang, H., Li, X., Amin, M. N., Al-Naghi, A. A. A., Arifeen, S. U., Althoey, F., & Ahmad, A. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.108543 · Cited by 27
Integrating a differential evolution feature weighting scheme into prototype generation
Isaac Triguero, A., Derrac, J., García, S., & Herrera, F. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.06.009 · Cited by 24
“Some of them are based on ReliefF [50] which is the first step of development of Relief-based methods as FW techniques [51].”
MethodChallenge balancing for personalised game spaces
Bakkes, S. C. J., Whiteson, S. A., Li, G., et al. (2014) · doi:10.1109/gem.2014.7047971 · Cited by 24
Semi-supervised minimum redundancy maximum relevance feature selection for audio classification
Yang, X. K., He, L. J., Qu, D., et al. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4287-0 · Cited by 24
“However, it should also be noticed that the performances of CCLS and ReliefF do not drop rapidly when decreasing the number of labeled data to 200, while the CS and Spec algorithms are unable to select relevant features.”
MethodInfluentialSubtype dependent biomarker identification and tumor classification from gene expression profiles
Wang, A., An, N., Chen, G., et al. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.01.025 · Cited by 24
Channel Selection Improves MEG-based Brain-Computer Interface
Roy, S., Rathee, D., McCreadie, K. A., et al. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8716948 · Cited by 24
“The Relief algorithm proposed by Kira et al. [18], [19] estimates the quality of the attributes that have weights greater than the thresholds using the difference of an attribute value between a given instance and the two nearest instances (Hit and Miss).”
MethodEpistasis, Complexity, and Multifactor Dimensionality Reduction
Pan, Q., Hu, T., & Moore, J. H. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-447-0_22 · Cited by 24
“Kononenko improved upon Relief by choosing n nearest neighbors instead of just one [51–53].”
BackgroundExploring the representativeness of the M5 competition data
Theodorou, E., Wang, S., Kang, Y., et al. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.07.006 · Cited by 24
Genomic mining for complex disease traits with “random chemistry”
Eppstein, M. J., Payne, J. L., White, B. C., & Moore, J. H. (2007) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-007-9039-5 · Cited by 24
“However, to date, the most effective fitness approximator we have achieved is based on the ReliefF data mining algorithm [25], which has previously been shown to have promise for a similar SNP problem [19].”
MethodIdentifying biologically relevant genes via multiple heterogeneous data sources
Zhao, Z., Wang, J., Liu, H., et al. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401990 · Cited by 24
“In this work we implement MSGS-kerCB: we use the approach proposed in [31] to learn the combination coecients 7. For comparison we also include a supervised selection algorithm ReliefF [ 24 ] in the experiment.”
MethodTumor classification based on DNA copy number aberrations determined using SNP arrays
Wang, Y., Makedon, F. S., & Pearlman, J. D. (2006) · Oncology Reports · doi:10.3892/or.15.4.1057 · Cited by 24
“This approach has shown good performance in various domains [11].”
BackgroundVery large scale ReliefF for genome-wide association analysis
Eppstein, M. J., & Haake, P. (2008) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2008.4675767 · Cited by 24
“The ReliefF data mining algorithm [26] estimates importance weights of individual attributes by comparing similarities between samples across entire attribute sets simultaneously, in case/control studies.”
BackgroundMethodInfluentialLiu, H. (2010) · Encyclopedia of Machine Learning · doi:10.1007/978-0-387-30164-8_306 · Cited by 24
Multiview Feature Selection With Information Complementarity and Consensus for Fault Diagnosis
Wang, G., Zhang, F., & Li, Z. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3260100 · Cited by 24
“We compare our proposed MFSICC with 13 feature selection methods, including eight traditional feature selection methods (i.e., LS [22], reliefF [46], CS [47], RFE [24], RF [48], Lasso [49], RFE [38], and SGL [40]) and five state-of-the-art multiview feature selection methods (i.e., WMCFS [32], MVML…”
MethodFeng, X., Zainudin, E. B., Wong, H. W., et al. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113083 · Cited by 23
Demir, F. (2025) · IEEE Access · doi:10.1109/access.2025.3551749 · Cited by 24
Zhang, Y., & Li, T. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2089094.2089100 · Cited by 23
Real-time data mining of massive data streams from synoptic sky surveys
Djorgovski, S. G., Graham, M. J., Donalek, C., Mahabal, A. A., Drake, A. J., Turmon, M., & Fuchs, T. J. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2015.10.013 · Cited by 23
A Double Weighted Naive Bayes with Niching Cultural Algorithm for Multi-Label Classification
Yan, X., Wu, Q., & Sheng, V. S. (2016) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001416500130 · Cited by 23
Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture
Škrlj, B., Kralj, J., Lavrač, N., & Pollak, S. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1020034 · Cited by 26
“(Note that this step of the approach could be possibly improved using e.g., the RelieF) [46] branch of algorithms.”
MethodVehicle Classification Based on Feature Selection With Anisotropic Magnetoresistive Sensor
Zhang, X., & Huang, H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2928828 · Cited by 23
“When dealing with multi-classification problems, ReliefF randomly selects an instance R, and then searches for k of its nearest neighbors from the same class, called nearest hits H j , and also k nearest neighbors from each of the different classes, called nearest misses M j (C) [17].”
BackgroundSiva Subramanian, R., & Prabha, D. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01961-9 · Cited by 23
RetractedEstimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors
Fengou, L.-C., Mporas, I., Spyrelli, E. D., Lianou, A., & Nychas, G. E. (2020) · IEEE Access · doi:10.1109/access.2020.3000690 · Cited by 24
“and Y-variables (find combinations of the predictors (Xvariables) that have a large covariance with the response (target) values (Y-variables)) and is used for data with many, noisy and collinear variables [29], [30]; (c) ReliefF is a feature rankingmethod, which evaluates the worth of an attribute by repeatedly sampl…”
MethodClustering-based feature subset selection with analysis on the redundancy–complementarity dimension
Chen, Z., Chen, Q., Zhang, Y., et al. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.01.005 · Cited by 23
Jamei, M., Elbeltagi, A., Maroufpoor, S., Karbasi, M., Jamei, M., Jalali, M., & Najafzadeh, N. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106687 · Cited by 23
Ma, J., Zhang, A., Gao, F., et al. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03803-x · Cited by 23
“In this section, an attribute selection method for distributed EBRB generation based on Relief-F algorithm [24, 25] is proposed to select the representative antecedent attributes.”
MethodKannadasan, K., Veerasingam, S., Begum, B. S., & Ramasubramanian, N. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01762-w · Cited by 23
“ReliefF and chi-square test algorithms fail to consider the correlation between the features, but the features extracted from the EEG signals are highly correlated with each other in the DEAP dataset.”
MethodInfluentialAhi, Y., Dilcan, Ç. C., Köksal, D. D., et al. (2022) · Water Resources Management · doi:10.1007/s11269-022-03365-0 · Cited by 23
Wan, M., Wang, W., Zhang, W., et al. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110451 · Cited by 23
Greene, C. S., Gilmore, J. M., Kiralis, J., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01184-9_9 · Cited by 23
“This new algorithm, ReliefF, has been shown to be more robust to noisy attributes and missing data and is widely used in data mining applications [19].”
MethodZeng, Z., Zhang, H., Zhang, R., et al. (2014) · ISRN Applied Mathematics · doi:10.1155/2014/382738 · Cited by 23
Karoui, I., Fablet, R., Boucher, J.-M., et al. (2008) · Pattern Analysis and Applications · doi:10.1007/s10044-008-0108-z · Cited by 23
“based ‘‘Filter methods for feature selection’’ [31–34] that estimate the quality of attributes according to how well their values distinguish between samples that are near to each other with respect to the euclidean distance, we compute the margin according to Kullback-Leibler and we evaluate the margin with respect t…”
BackgroundMethodEnhanced analytical power of SDS‐PAGE using machine learning algorithms
Supek, F., Štefanić, P. P., Krsnik‐Rasol, M., & Šmuc, T. (2007) · PROTEOMICS · doi:10.1002/pmic.200700555 · Cited by 23
“Relative importance of each PC for the three discrimination tasks was estimated using the ReliefF scheme [14], a heuristic approach to rank attributes in context of possible nonlinear interactions between them.”
MethodLinear regression-based feature selection for microarray data classification
Hasan, M. A., Hasan, M. K., & Mottalib, M. A. (2015) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2015.066776 · Cited by 23
Feature Selection Approach for Intrusion Detection System
Kumar, K., Kumar, G., & Kumar, Y. (2013) · Cited by 23
Dimensional music emotion recognition by valence-arousal regression
Bai, J., Peng, J., Shi, J., et al. (2016) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2016.7862063 · Cited by 23
AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity
Sun, L., Wang, J., & Wei, J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1468-4 · Cited by 23
“This metric is based on the similarity of instances inspired by the state-of-the-art feature selection method ReliefF [23], which adopts the nearest neighbor rule to evaluate features.”
MethodA variable selection method for multiclass classification problems using two-class ROC analysis
de Figueiredo, M., Cordella, C., Bouveresse, D. J., et al. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2018.04.005 · Cited by 23
“ReliefF randomly selects an instance and searches for its k nearest neighbors belonging to the same class, called hits, and its k nearest neighbors belonging to each of the different classes, called misses [18].”
BackgroundParameter estimation for optimal asteroid transfer trajectories using supervised machine learning
Shang, H., Wu, X., Qiao, D., et al. (2018) · Aerospace Science and Technology · doi:10.1016/j.ast.2018.06.002 · Cited by 23
Bellino, G. M., Schiaffino, L., Battisti, M. A. M., et al. (2019) · Entropy · doi:10.3390/e21040346 · Cited by 23
“When considering that instances are randomly sampled from data, then the score of the i-th feature Si is defined according to Equation (14), where Mk denotes the values in the i-th feature of the closest instances Xk with same class tag, while Hk denotes the values in the i-th function of the instances that are closes…”
BackgroundMethodAbdellaoui, Y., Olguín, M. T., Abatal, M., et al. (2019) · Desalination and Water Treatment · doi:10.5004/dwt.2019.23792 · Cited by 23
Energy Harvesting-Based Smart Transportation Mode Detection System via Attention-Based LSTM
Xu, W., Feng, X., Wang, J., Luo, C., Li, J., & Ming, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2918555 · Cited by 23
“There are a number of techniques that can be used to determine feature quality and select features, such as information gain [36] and ReliefF algorithm [37].”
MethodSmart user identification using cardiopulmonary activity
Shah, S. W. A., & Kanhere, S. S. (2019) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2019.05.005 · Cited by 23
Entropy of mitochondrial DNA circulating in blood is associated with hepatocellular carcinoma
Campo, D. S., Nayak, V., Srinivasamoorthy, G., et al. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0506-7 · Cited by 23
Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning
Zhou, Z., Lin, Y., Zhang, Z., Wu, Y., & Johnson, P. A. (2019) · Seismological Research Letters · doi:10.1785/0220180315 · Cited by 23
“Relief-F improves Relief by k-nearest neighbors of each class [7].”
BackgroundLiu, Y., Shi, X.-J., & Xu, Y. H. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105658 · Cited by 23
Classification of different liquid milk by near-infrared spectroscopy and ensemble modeling
Chen, H., Tan, C., Lin, Z., et al. (2021) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2021.119460 · Cited by 23
SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis
Tadist, K., Mrabti, F., Nikolov, N. S., et al. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00409-x · Cited by 23
Hedjazi, L., Aguilar-Martín, J., Lann, M. V. L., & Kempowsky-Hamon, T. (2015) · Information Sciences · doi:10.1016/j.ins.2015.06.007 · Cited by 23
“Many works can be also found in literature comparing Relief and Simba with information-theoretic and probabilistic approaches [54,66,86,87].”
BackgroundBertin, G. I., Sabbagh, A., Guillonneau, F., Jafari-Guemouri, S., Ezinmegnon, S., Federici, C., Hounkpatin, B., Fievet, N., & Deloron, P. (2013) · The Journal of Infectious Diseases · doi:10.1093/infdis/jit377 · Cited by 21
“ReliefF is an extension of the Relief algorithm [39].”
MethodMa, C., Sun, Y., Zhang, P., Song, F., Feng, Y., He, Y., & Zhang, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3342856 · Cited by 24
“Restrictions apply. algorithm Relief [33], weakly supervised feature (WSF) selection algorithm based on spectral analysis [34], max-relevance and min-redundancy (mRMR) algorithm based on mutual information [35], and unsupervised multicluster feature selection (MCFS) [36].”
MethodHuman identification using WiFi signal
Zhang, J., Wei, B. W., Hu, W. S., et al. (2016) · IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457075 · Cited by 22
“These feature subsets are carefully chosen via feature selection algorithms [4] beforehand.”
MethodHamzeh, S., Mokarram, M., Haratian, A., et al. (2016) · Agriculture · doi:10.3390/agriculture6040052 · Cited by 22
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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