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Theoretical and Empirical Analysis of ReliefF and RReliefF
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Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,498
A Practical Approach to Feature Selection
Kira, K., & Rendell, L. A. (1992) · Elsevier eBooks · doi:10.1016/b978-1-55860-247-2.50037-1 · Cited by 3,090
Estimating attributes: Analysis and extensions of RELIEF
Kononenko, I. (1994) · Lecture notes in computer science · doi:10.1007/3-540-57868-4_57 · Cited by 3,062
Self-citationAn Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
The feature selection problem: traditional methods and a new algorithm
Kira, K., & Rendell, L. A. (1992) · National Conference on Artificial Intelligence · Cited by 1,828
Research works printed but not linked: 6
- Dietterich, T. G. (1997). Machine learning research: Four current directions. AI Magazine, 18:4, 97-136.
- Jovanoski, V., & Lavrač, N. (1999). Feature subset selection in association rules learning systems. In M. Grobelnik, & D. Mladenič (Eds.), Prooceedings of the Conference Analysis, Warehousing and Mining the Data (AWAMIDA'99) (pp. 74-77).
- Robnik, M. (1995). Constructive induction in machine learning. Electrotehnical Review, 62:1, 43-49. (in Slovene).
- Robnik Šikonja, M. (1998). Speeding up relief algorithm with k-d trees. In Proceedings of Electrotehnical and Computer Science Conference (ERK'98) (pp. B:137-140). Portorož, Slovenia.
- Robnik Šikonja, M., & Kononenko, I. (1996). Context sensitive attribute estimation in regression. In M. Kubat, & G. Widmer (Eds.), Proceedings of ICML'96 Workshop on Learning in Context Sensitive Domains (pp. 43-52). Morgan Kaufmann.
- Vilalta, R. (1999). Understanding accuracy performance through concept characterization and algorithm analysis. In Proceedings of the ICML-99 Workshop on Recent Advances in Meta-Learning and Future Work (pp. 3-9).
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Medeiros, J., Couceiro, R., Duarte, G., Duraes, J. A., Castelhano, J., Duarte, I. C., Castelo-Branco, M. S., Madeira, H., Carvalho, P., & Teixeira, C. (2021) · Sensors · doi:10.3390/s21072338 · Cited by 44
“Another ranking method used was the robust and noise tolerant ReliefF Algorithm [80,81].”
MethodMulti-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparative Analysis
Panda, R., Malheiro, R., Rocha, B., et al. (2013) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 43
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“Moreover, the weight of each band is calculated using the “ReliefF” analysis with ten nearest neighbors per class [25].”
MethodFisher, T. B., Saini, G., Rekha, T. S., Krishnamurthy, J., Bhattarai, S., Callagy, G., Webber, M., Janssen, E. A. M., Kong, J., & Aneja, R. (2024) · Breast Cancer Research · doi:10.1186/s13058-023-01752-y · Cited by 42
“The ReliefF importance weights were used to assess the significance of the selected TME graph features [54–56, 67, 68].”
MethodGalli, A., Piscitelli, M. S., Moscato, V., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117649 · Cited by 42
Computational intelligence technique for early diagnosis of heart disease
Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2015) · IEEE International Conference on Engineering and Technology · doi:10.1109/icetech.2015.7275001 · Cited by 42
Discriminative Feature Selection by Nonparametric Bayes Error Minimization
Yang, S., & Hu, B.-G. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.92 · Cited by 42
“Among the existing feature weighting methods, the Relief algorithm [20], [26] is considered one of the most successful techniques due to its effectiveness and simplicity [25].”
Machine learning based typology development in archaeology
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A Variance Reduction Framework for Stable Feature Selection
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Ramzan, Z., Asif, H. M. S., Yousuf, M. I., & Shahbaz, M. A. (2023) · IEEE Access · doi:10.1109/access.2023.3271410 · Cited by 41
“In this study, a combination of two techniques [3], [4] is used which includes ReliefF [57] and sequential feature selection approaches [2].”
MethodInfluentialFu, J., Wang, M., Chen, B., et al. (2023) · Engineering With Computers · doi:10.1007/s00366-023-01841-8 · Cited by 41
“x̃j ] [86], F-test importance score F [6], and nearest-neighbor-based feature weight f (w) [116].”
Evolutionary feature weighting to improve the performance of multi-label lazy algorithms
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“The most relevant feature weighting methods that have appeared in the context of multi-label data are based on the Relief family of algorithms [29,34,48].”
Forecasting ATM cash demands using a local learning model of cerebellar associative memory network
Teddy, S. D., & Ng, S. K. (2010) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2010.02.013 · Cited by 41
All-relevant feature selection using multidimensional filters with exhaustive search
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“Unfortunately, it is biased against weaker and correlated variables [35].”
BackgroundA fast unsupervised preprocessing method for network monitoring
Lopez, M. A., Mattos, D. M. F., Duarte, O. C. M. B., & Pujolle, G. (2018) · Annals of Telecommunications · doi:10.1007/s12243-018-0663-2 · Cited by 41
“However, embedded methods make the feature selection during the learning process and use its properties to guide feature evaluation.”
BackgroundMethodA factor graph model for unsupervised feature selection
Wang, H., Zhang, Y., Zhang, J., et al. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.034 · Cited by 41
A new dynamic security assessment framework based on semi-supervised learning and data editing
Liu, R., Verbič, G., & Ma, J. (2019) · Electric Power Systems Research · doi:10.1016/j.epsr.2019.03.009 · Cited by 41
Diatom identification including life cycle stages through morphological and texture descriptors
Sánchez, C., Cristóbal, G., & Bueno, G. (2019) · PeerJ · doi:10.7717/peerj.6770 · Cited by 41
Dog behaviour classification with movement sensors placed on the harness and the collar
Kumpulainen, P., Cardó, A. V., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Antink, C. H., Surakka, V., Kujala, M. V., et al. (2021) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2021.105393 · Cited by 39
Wang, J., Li, Q., Zhang, H., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118419 · Cited by 41
Hidden Risk Genes with High-Order Intragenic Epistasis in Alzheimer's Disease
Sun, J., Song, F., Wang, J., Han, G., Bai, Z., Xie, B., Feng, X., Jia, J., Duan, Y., & Lei, H. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-140054 · Cited by 41
“Widely used software packages based on LR include PLINK [16] machine learning methods include Random Forests [19, 20], ReliefF [21], and multifactor dimensionality reduction (MDR) [22, 23].”
Multiple-Criteria Linear Programming for VIP E-Mail Behavior Analysis
Zhang, P., & Dai, J. (2007) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2007.45 · Cited by 41
“Another possibility is to use a filter method such as Relief [14] in conjunction with an SVM.”
MethodLee, S. I., Park, E., Huang, A., Mortazavi, B., Hayward Garst, J., Jahanforouz, N., Espinal, M., Siero, T., Pollack, S., Afridi, M., et al. (2016) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.02.004 · Cited by 41
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Fragment-level classification of ECG arrhythmia using wavelet scattering transform
Nahak, S., Pathak, A., & Saha, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120019 · Cited by 40
Moaveninejad, S., D’Onofrio, V., Tecchio, F., Ferracuti, F., Iarlori, S., Monteriù, A., & Porcaro, C. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107944 · Cited by 40
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An optimization of ReliefF for classification in large datasets
Huang, Y., McCullagh, P. J., & Black, N. D. (2009) · Data & Knowledge Engineering · doi:10.1016/j.datak.2009.07.011 · Cited by 40
“It will also result in underestimating numerical attributes [31].”
BackgroundTransmission line fault location using hybrid wavelet-Prony method and relief algorithm
Farshad, M., & Sadeh, J. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.03.045 · Cited by 40
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DeepACTION: A deep learning-based method for predicting novel drug-target interactions
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Alam, T. M., Mushtaq, M., Shaukat, K., Hameed, I. A., Sarwar, M. U., & Luo, S. (2021) · Applied Sciences · doi:10.3390/app11199296 · Cited by 40
“The Relief-F algorithm [20] is an improved version of the Relief algorithm used for classification problems with more than two classes.”
MethodRobust Joint Feature Weights Learning Framework
Yan, H., Yang, J. J., & Yang, J. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2515613 · Cited by 40
“The nonnegative property ensures the physical significance of learned feature weights.”
BackgroundAutomatic eczema classification in clinical images based on hybrid deep neural network
Rasheed, A., Umar, A. I., Shirazi, S. H., Khan, Z., Nawaz, S., & Shahzad, M. (2022) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2022.105807 · Cited by 36
Spolaôr, N., Cherman, E. A., Monard, M. C., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34459-6_8 · Cited by 39
“1 respectively, which can be considered conservative [11].”
BackgroundArtificial immune system for attribute weighted Naive Bayes classification
Wu, J. X., Cai, Z., Zeng, S., et al. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706818 · Cited by 39
Improving the accuracy of long-term travel time prediction using heterogeneous ensembles
Mendes‐Moreira, J., Jorge, A., de Sousa, J. F., & Soares, C. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.08.072 · Cited by 39
Feature Selection via Cramer's V-Test Discretization for Remote-Sensing Image Classification
Wu, B., Zhang, L., & Zhao, Y. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2263510 · Cited by 39
“criterion (mRMR) [14], [15], relief feature selection (ReliefF) [16], cluster-based feature selection [17], and correlation-based”
BackgroundDatta, A., Ghosh, S., & Ghosh, A. K. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.871392 · Cited by 39
On feature selection protocols for very low-sample-size data
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“ReliefF [37] , a variant of Relief [36] , is an instance-based feature ranking method.”
MethodInfluentialLazzaro, G. D., Ricci, M., Al-Wardat, M. S., Schirinzi, T., Scalise, S., Giannini, F., Mercuri, N. B., Saggio, G., & Pisani, A. (2019) · Journal of Parkinson s Disease · doi:10.3233/jpd-191758 · Cited by 39
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Seyedi, Y., Karimi, H., & Mahseredjian, J. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3142110 · Cited by 39
“Therefore, the differences between stable and unstable feature sequences are magnified which in turn increase the overall prediction reliability [26].”
BackgroundShang, Y., Zheng, X., Li, J., et al. (2022) · Remote Sensing · doi:10.3390/rs14133019 · Cited by 39
Towards automated eye cancer classification via VGG and ResNet networks using transfer learning
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(2007) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2007.192 · Cited by 39
“Then, the ReliefF algorithm for feature selection [11] is applied to the produced dataset to reduce the dimensionality.”
Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport
Ding, Y., Li, Y., Deng, K., Tan, H., Yuan, M., & Ni, L. M.-S. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2623320 · Cited by 38
“For example, Fisher score [30], where features are scored by considering that features with high quality should assign similar values to instances in the same class and different values to instances from different classes; and ReliefF [31], [32], which selects features to separate instance from different classes.”
BackgroundFeature Selection for Handwritten Word Recognition Using Memetic Algorithm
Ghosh, M., Malakar, S., Bhowmik, S., et al. (2018) · Studies in computational intelligence · doi:10.1007/978-981-10-8974-9_6 · Cited by 38
Gärtner, D., & Padman, R. (2019) · Journal of the Operational Research Society · doi:10.1080/01605682.2019.1571005 · Cited by 38
Nag, K., & Pal, N. R. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2927526 · Cited by 40
“The FS algorithms are: 1) FAST [69]; 2) FCBF [73], [74]; 3) CFS [75]; 4) ReliefF [76]; 5) Consist [77]; and 6) FOCUS-SF [78].”
Xiao, F., Yang, D., Lv, Z., et al. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.11.025 · Cited by 38
Krmar, J., Vukičević, M., Kovačević, A., et al. (2020) · Journal of Chromatography A · doi:10.1016/j.chroma.2020.461146 · Cited by 38
Efficient approach for EEG‐based emotion recognition
Şengür, D., & Siuly, S. (2020) · Electronics Letters · doi:10.1049/el.2020.2685 · Cited by 40
“The relief algorithm is considered for feature selection [15].”
MethodA novel self-learning feature selection approach based on feature attributions
Chen, J., Yuan, S., Lv, D., et al. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115219 · Cited by 38
Rahman, A., Xi, M., Dabrowski, J. J., et al. (2021) · Aquacultural Engineering · doi:10.1016/j.aquaeng.2021.102192 · Cited by 38
Aljouie, A., Almazroa, A. A., Bokhari, Y., Alawad, M., Mahmoud, E., Alawad, E., Alsehawi, A., Rashid, M., Alomair, L., Almozaai, S., et al. (2021) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s322431 · Cited by 39
Image processing and machine learning for fully automated probabilistic evaluation of medical images
Šajn, L., & Kukar, M. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.06.021 · Cited by 38
“Attributes were filtered with the ReliefF algorithm [42,32].”
MethodInfluentialA case-based reasoning system for PCB principal process parameter identification
Tsai, C., & CHIU, C. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.02.014 · Cited by 38
“Another feature selection method is ReliefF algorithm (Robnik-Sikonja & Kononenko, 2003).”
MethodRandom feature selection using random subspace logistic regression
Wichitaksorn, N., Kang, Y., & Zhang, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119535 · Cited by 37
Weighted Hybrid Decision Tree Model for Random Forest Classifier
Kulkarni, V. Y., Sinha, P. K., & Petare, M. C. (2015) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-014-0176-y · Cited by 37
“‘RRe-liefF’ is used for regression problems [17].”
Local and Global Discriminative Learning for Unsupervised Feature Selection
Du, L., Shen, Z., Li, X., et al. (2013) · Proceedings · doi:10.1109/icdm.2013.23 · Cited by 37
“The typical methods include Fisher score, information gain, and relief [16].”
MethodKimovski, D., Ortega, J., Ortíz, A., & Baños, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.061 · Cited by 37
McKinney, B. A., White, B. C., Grill, D. E., Li, P. W., Kennedy, R. B., Poland, G. A., & Oberg, A. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081527 · Cited by 37
“Moreover, as k increases, Relief-F’s estimates of feature importance become more myopic, making the scores behave more like a univariate statistic [17].”
BackgroundPatel, S., Chen, B.-R., Mancinelli, C., Paganoni, S., Shih, L. C., Welsh, M., Dy, J., & Bonato, P. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090452 · Cited by 37
“The ReliefF algorithm iterates through every instance updating the weights assigned to a feature based on its ability to correctly classify the instance.”
MethodInfluentialA Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction
Haider, S., Rahman, R., Ghosh, S., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144490 · Cited by 37
“To avoid any bias due to feature selection in variable importance, we consider the full set of probe set ids without application of RELIEFF for this analysis.”
InfluentialA new weighted naive Bayes method based on information diffusion for software defect prediction
Ji, H., Huang, S., Wu, Y., Hui, Z., & Zheng, C. (2019) · Software Quality Journal · doi:10.1007/s11219-018-9436-4 · Cited by 37
“ReliefF is an instance-based feature-weighting technique that was introduced by Kira and Rendell (1992) and Robnikšikonja and Kononenko (2003), and it can also be used to reveal the relationship between the features and the class.”
InfluentialFeature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm
Li, S., Zhang, K., Chen, Q., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3012768 · Cited by 37
“Therefore, in order to reduce the learning dimension and achieve high accuracy in an acceptable time, feature selection is often used as an important data preprocessing process for machine learning [7]–[9], which can distinguish and discard useless features in the classification learning process according to some crit…”
Li, A., & He, Z. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106852 · Cited by 37
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A Multidimensional Feature Extraction and Selection Method for ECG Arrhythmias Classification
Yang, J., & Yan, R. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3047962 · Cited by 37
Li, Q., Wang, J., & Zhang, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107435 · Cited by 37
Prediction of wind turbine blades icing based on feature Selection and 1D-CNN-SBiGRU
Li, Y., Hou, L., Tang, M., Sun, Q., Chen, J., Song, W., Yao, W. F., & Cao, L. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11700-7 · Cited by 37
“The ReliefF algorithm is an extension of the Relief algorithm, usually used for feature selection of multi-category tags [21, 34].”
MethodExercise training enhances muscle mitochondrial metabolism in diet-resistant obesity
Pileggi, C. A., Blondin, D. P., Hooks, B. G., Parmar, G., Alecu, I., Patten, D. A., Cuillerier, A., O’Dwyer, C., Thrush, A. B., Fullerton, M. D., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104192 · Cited by 37
A Hybrid Feature Selection Algorithm: Combination of Symmetrical Uncertainty and Genetic Algorithms
Jiang, B.-N., Ding, X., Ma, L., et al. (2008) · Cited by 37
Mokhtari, F., & Hossein‐Zadeh, G. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.10.012 · Cited by 38
Estimation of Seismic Vulnerability Levels of Urban Structures With Multisensor Remote Sensing
Geiß, C., Jilge, M., Lakes, T., & Taubenböck, H. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2442584 · Cited by 37
“ues for instances from the same class [65], [67].”
BackgroundWang, B., Wang, W., Qiao, Z., et al. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104892 · Cited by 36
An adaptive pyramid PSO for high-dimensional feature selection
Xiao, J., Wei, B., Deng, L., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125084 · Cited by 36
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Sun, X., Liu, Y., Wei, D., et al. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.10.004 · Cited by 36
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
Ngo-Ye, T. L., & Sinha, A. P. (2012) · ACM Transactions on Management Information Systems · doi:10.1145/2229156.2229158 · Cited by 36
Research of new strategies for improving CBR system
Guo, Y., Hu, J., & Peng, Y. (2012) · Artificial Intelligence Review · doi:10.1007/s10462-012-9327-1 · Cited by 36
“Another feature selection method is ReliefF algorithm (Robnik-Sikonja and Kononenko 2003).”
MethodFeature Selection for Nonlinear Kernel Support Vector Machines
Mangasarian, O. L., & Kou, G. (2007) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2007.30 · Cited by 36
“Another possibility is to use a filter method such as Relief [14] in conjunction with an SVM.”
MethodModel Development and Validation
Trendowicz, A. (2012) · The Fraunhofer IESE series on software and systems engineering · doi:10.1007/978-3-642-30764-5_5 · Cited by 36
Improving matrix factorization recommendations for examples in cold start
Ocepek, U., Rugelj, J., & Bosnić, Z. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.04.071 · Cited by 36
“Both iterate over all available attributes; in each iteration they treat the current attribute as the target attribute and all others as input attributes and evaluate attributes using: IG (information gain (Kent, 1983)), and RR (RReliefF (Robnik-Šikonja & Kononenko, 2003)).”
MethodCoSelect: Feature Selection with Instance Selection for Social Media Data
Tang, J., & Liu, H. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.77 · Cited by 36
“The filter model relies on general characteristics of the data to evaluate and select feature subsets without involving any mining algorithm [30, 31, 32, 24].”
MethodBehbahani, H., Amiri, A. M., Imaninasab, R., et al. (2018) · Journal of Forecasting · doi:10.1002/for.2542 · Cited by 36
Binary $$\beta$$-hill climbing optimizer with S-shape transfer function for feature selection
Al‐Betar, M. A., Hammouri, A. I., Awadallah, M. A., & Doush, I. A. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02484-z · Cited by 36
Unsupervised feature selection by non-convex regularized self-representation
Miao, J., Ping, Y., Chen, Z., Jin, X., Li, P., & Niu, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114643 · Cited by 36
Comprehensible evaluation of prognostic factors and prediction of wound healing
Robnik‐Šikonja, M., Cukjati, D., & Kononenko, I. (2003) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(03)00044-7 · Cited by 36
“The mean absolute error(MAE) criterion is similar to MSE, the only difference being in Equation (5), where instead of squared difference the absolute difference is used.”
BackgroundSelf-citationA variance reduction framework for stable feature selection
Han, Y., & Lei, Y. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11152 · Cited by 36
“In contrast with margin based feature selection algorithms (e.g., ReliefF [ 13 ]) which directly use margins to weight features, our algorithm exploits the discrepancies among the margins at various instances to weight instances.”
MethodInfluentialStability of Feature Selection Algorithms and Ensemble Feature Selection Methods in Bioinformatics
Yang, P., Zhou, B. B., Yang, J. Y. H., et al. (2013) · doi:10.1002/9781118617151.ch14 · Cited by 36
“This is best exemplified by using ReliefF based feature selection algorithms [29] for categorical feature selection.”
MethodTalebi, H., Bahrami, B., Ahmadian, H., Nejati, M., & Ayatollahi, M. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.136783 · Cited by 38
Model mining for robust feature selection
Woźnica, A., Nguyen, P., & Kalousis, A. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339674 · Cited by 35
“RELIEF delivers a weighting of the features by computing distances among each of the training instances and their 10-nearest neighbors, and estimating the contribution of each feature in these distances.”
MethodInfluentialQian, K., Xu, Z., Xu, H., et al. (2015) · IET Signal Processing · doi:10.1049/iet-spr.2013.0266 · Cited by 35
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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