The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
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).
Cited by
Sorted by citations
Feature Selection: A Data Perspective
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
Efficient Feature Selection via Analysis of Relevance and Redundancy
Yu, L., & Liu, H. (2004) · Cited by 1,969
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
“[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
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
“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).”
BackgroundMethodResultInfluential
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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