Theoretical and Empirical Analysis of ReliefF and RReliefF
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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 23,834 · see citing works
(1992) · Cited by 23,431 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
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 21,028 · see citing works
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417 · see citing works
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,659 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
UCI Repository of Machine Learning Databases
Murphy, P. M. (1994) · Medical Entomology and Zoology · Cited by 1,803 · see citing works
Knowledge Discovery in Databases
Piateski, G., & Frawley, W. (1991) · Medical Entomology and Zoology · Cited by 1,746 · see citing works
Quinlan (2003) · Cited by 997 · see citing works
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Kononenko, I., Šimec, E., & Robnik‐Šikonja, M. (1997) · Applied Intelligence · doi:10.1023/a:1008280620621 · Cited by 801 · see citing works
Self-citationSedgewick, R. (1990) · Medical Entomology and Zoology · Cited by 723 · see citing works
Wettschereck, D., Aha, D. W., & Mohri, T. (1997) · Artificial Intelligence Review · doi:10.1023/a:1006593614256 · Cited by 717 · see citing works
Simon, H. A., Hunt, E. B., Marin, J., & Stone, P. (1967) · The American Journal of Psychology · doi:10.2307/1421207 · Cited by 659 · see citing works
Hunt, E. B., Marin, J., & Stone, P. J. (1966) · Medical Entomology and Zoology · Cited by 646 · see citing works
An adaptation of Relief for attribute estimation in regression
Robnik‐Šikonja, M., & Kononenko, I. (1997) · Cited by 478 · see citing works
Self-citationThe MONK's problems: A Performance Comparison of Different Learning Algorithms
Thrun, S., Bala, J. W., Bloedorn, E., et al. (1991) · doi:10.13021/mars/3370 · Cited by 447 · see citing works
Self-citationASSISTANT 86: a knowledge-elicitation tool for sophisticated users
Cestnik, B., Kononenko, I., & Bratko, I. (1987) · Medical Entomology and Zoology · Cited by 436 · see citing works
Self-citationMethodologies for Intelligent Systems
(1991) · Lecture notes in computer science · doi:10.1007/3-540-54563-8 · Cited by 404 · see citing works
On biases in estimating multi-valued attributes
Kononenko, I. (1995) · Cited by 281 · see citing works
Self-citationRule Induction Using Information Theory.
Smyth, P., & Goodman, R. M. (1991) · Cited by 239 · see citing works
Analysing and improving the diagnosis of ischaemic heart disease with machine learning
Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., & Fettich, J. (1999) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(98)00063-3 · Cited by 165 · see citing works
Self-citation(1994) · Lecture notes in computer science · doi:10.1007/3-540-57868-4 · Cited by 136 · see citing works
Use of contextual information for feature ranking and discretization
Hong, S. J. (1997) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.634751 · Cited by 129 · see citing works
Efficient Locally Weighted Polynomial Regression Predictions
Moore, A., Schneider, J., & Deng, K. (1997) · Cited by 100 · see citing works
Context-Sensitive Feature Selection for Lazy Learners
Domingos, P. (1997) · Springer eBooks · doi:10.1007/978-94-017-2053-3_9 · Cited by 98 · see citing works
Learning hard concepts through constructive induction: framework and rationale
Rendell, L. A., & Seshu, R. (1990) · Computational Intelligence · doi:10.1111/j.1467-8640.1990.tb00298.x · Cited by 97 · see citing works
Wettschereck, D., Aha, D. W., & Mohri, T. (1997) · Springer eBooks · doi:10.1007/978-94-017-2053-3_11 · Cited by 91 · see citing works
Learning a local similarity metric for case-based reasoning
Ricci⋆, F., & Avesani, P. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60598-3_27 · Cited by 73 · see citing works
Multiresolution instance-based learning
Deng, K., & Moore, A. (1995) · International Joint Conference on Artificial Intelligence · Cited by 70 · see citing works
Control-Sensitive Feature Selection for Lazy Learners
Domingos, F. (1997) · Artificial Intelligence Review · doi:10.1023/a:1006508722917 · Cited by 63 · see citing works
Induction of Decision Trees Using Relieff
Kononenko, I., & Šimec, E. (1995) · Springer eBooks · doi:10.1007/978-3-7091-2690-5_14 · Cited by 41 · see citing works
Self-citationLearning hard concepts through constructive induction: framework and rationale
Rendell, L. A., & Seshu, R. (1994) · Conference on Learning Theory · Cited by 40 · see citing works
Decision tree design using information theory
Goodman, R. M., & Smyth, P. (1990) · Knowledge Acquisition · doi:10.1016/s1042-8143(05)80020-2 · Cited by 24 · see citing works
Dalaka, A., Kompare, B., Robnik‐Šikonja, M., & Sgardelis, S. P. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00237-4 · Cited by 24 · see citing works
Self-citationLinear Space Induction in First Order Logic with Relieff
Pompe, U., & Kononenko, I. (1995) · Springer eBooks · doi:10.1007/978-3-7091-2690-5_13 · Cited by 18 · see citing works
Self-citationAutomatic Selection of Split Criterion during Tree Growing Based on Node Location
Brodley, C. E. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50018-9 · Cited by 17 · see citing works
Lubinsky, D. J. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50053-0 · Cited by 15 · see citing works
Learning despite concept variation by finding structure in attribute-based data
Pérez, E., & Rendell, L. A. (1996) · Cited by 12 · see citing works
Attribute Dependencies, Understandability and Split Selection in Tree Based Models
Robnik‐Šikonja, M., & Kononenko, I. (1999) · Cited by 9 · see citing works
Self-citationAn Algroithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1976) · Cited by 9 · see citing works
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
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).
Cited by
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Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442 · see citing works
Efficient Feature Selection via Analysis of Relevance and Redundancy
Yu, L., & Liu, H. (2004) · Cited by 1,969 · see citing works
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445 · see citing works
“[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
Cordell, H. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2579 · Cited by 1,350 · see citing works
Relief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311 · see citing works
“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 · see citing works
Feature Extraction and Selection for Emotion Recognition from EEG
Jenke, R., Peer, A., & BUSS, M. J. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2339834 · Cited by 1,087 · see citing works
“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
Cordell, H. J. (2002) · Human Molecular Genetics · doi:10.1093/hmg/11.20.2463 · Cited by 1,067 · see citing works
Feature selection for classification: A review
Tang, J., Alelyani, S., & Liu, H. (2014) · doi:10.1201/b17320-7 · Cited by 996 · see citing works
Spectral feature selection for supervised and unsupervised learning
Zhao, Z., & Liu, H. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273641 · Cited by 954 · see citing works
“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 · see citing works
Machine Learning: The art and science of algorithms that make sense of data
Flach, P. A. (2012) · Cited by 694 · see citing works
Nearest neighbor imputation algorithms: a critical evaluation
Beretta, L., & Santaniello, A. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0318-z · Cited by 679 · see citing works
“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.”
BackgroundMethodEnsemble approaches for regression
Mendes‐Moreira, J., Soares, C., Jorge, A., & de Sousa, J. F. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379786 · Cited by 665 · see citing works
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data
Song, Q., Ni, J., & Wang, G. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.181 · Cited by 664 · see citing works
An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Al-Zoubi, A. M., Mirjalili, S., & Fujita, H. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.009 · Cited by 661 · see citing works
“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 · see citing works
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 621 · see citing works
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 616 · see citing works
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 608 · see citing works
“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 · see citing works
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 · see citing works
“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 531 · see citing works
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 488 · see citing works
“, 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 · see citing works
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 444 · see citing works
“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 · see citing works
“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 418 · see citing works
A survey on swarm intelligence approaches to feature selection in data mining
Nguyen, B. H., Xue, B., & Zhang, M. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100663 · Cited by 410 · see citing works
Differential evolution for filter feature selection based on information theory and feature ranking
Hançer, E., Xue, B., & Zhang, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.10.028 · Cited by 393 · see citing works
“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 · see citing works
“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 · see citing works
Behmann, J., Mahlein, A., Rumpf, T., Römer, C., & Plümer, L. (2014) · Precision Agriculture · doi:10.1007/s11119-014-9372-7 · Cited by 389 · see citing works
“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 · see citing works
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 · see citing works
Trace ratio criterion for feature selection
Nie, F., Xiang, S., Jia, Y., et al. (2008) · Cited by 360 · see citing works
Feature Selection: An Ever Evolving Frontier in Data Mining
Liu, H., Motoda, H., Setiono, R., & Zhao, Z. (2010) · Cited by 353 · see citing works
Song, X., Yon, Z., Gong, D., et al. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3061152 · Cited by 349 · see citing works
“1) ReliefF FS (ReliefF) [60].”
Epistasis and Its Implications for Personal Genetics
Moore, J. H., & Williams, S. M. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.08.006 · Cited by 347 · see citing works
Deep feature weighting for naive Bayes and its application to text classification
Jiang, L., Li, C., Wang, S., et al. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.02.002 · Cited by 327 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 288 · see citing works
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 287 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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., Pedrycz, W., Yu, D., et al. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2024166 · Cited by 265 · see citing works
“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 268 · see citing works
“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 · see citing works
“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 255 · see citing works
“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 255 · see citing works
“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 273 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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 240 · see citing works
“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 239 · see citing works
“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 241 · see citing works
Searching for interacting features
Zhao, Z., & Liu, H. (2007) · International Joint Conference on Artificial Intelligence · Cited by 235 · see citing works
Kernelized Fuzzy Rough Sets and Their Applications
Hu, Q., Yu, D., Pedrycz, W., et al. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.260 · Cited by 235 · see citing works
“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 234 · see citing works
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 · see citing works
“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 · see citing works
Li, S., Wang, P., & Goel, L. (2015) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2015.2438322 · Cited by 230 · see citing works
“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., Lei Zhang, A., Chen, D., et al. (2010) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2010.01.004 · Cited by 228 · see citing works
“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 · see citing works
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 224 · see citing works
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 · see citing works
“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 226 · see citing works
Xiao, R., Hu, Q., & Li, J. (2019) · Measurement · doi:10.1016/j.measurement.2019.06.050 · Cited by 220 · see citing works
Generalized Fisher score for feature selection
Gu, Q., Li, Z., & Han, J. (2011) · Cited by 220 · see citing works
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 · see citing works
“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., et al. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3100056 · Cited by 217 · see citing works
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 · see citing works
“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 211 · see citing works
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 · see citing works
“equation to solve the multiclassification problem [35]:”
MethodQuadratic Programming Feature Selection
Rodríguez-Luján, I., Huerta, R., Elkan, C., et al. (2010) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 209 · see citing works
ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY
Dubey, R., Zhou, J.-Y., Wang, Y.-L., Thompson, P., & Ye, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.005 · Cited by 204 · see citing works
“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 · see citing works
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 201 · see citing works
“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 201 · see citing works
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 · see citing works
“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., et al. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.02.025 · Cited by 198 · see citing works
Wang, Y., Makedon, F. S., Ford, J., et al. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti192 · Cited by 196 · see citing works
“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 · see citing works
“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 176 · see citing works
“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 190 · see citing works
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 189 · see citing works
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 · see citing works
“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 · see citing works
“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 183 · see citing works
“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
- 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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