The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Schaffer, C. (1993) · Machine Learning · doi:10.1023/a:1022653209073 · Cited by 310
Cited by
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
(1998) · Cited by 3,476
A Survey on the Explainability of Supervised Machine Learning
Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766
Classifier Technology and the Illusion of Progress
Hand, D. J. (2006) · Statistical Science · doi:10.1214/088342306000000060 · Cited by 723
(2011) · Cited by 675
The promise of machine learning in predicting treatment outcomes in psychiatry
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531
The Role of Occam's Razor in Knowledge Discovery
Domingos, P. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009868929893 · Cited by 459
(2015) · International Conference on Artificial Intelligence and Statistics · Cited by 162
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12227 · Cited by 220
Automated Diagnosis for UMTS Networks Using Bayesian Network Approach
Khanafer, R. M., Solana, B., Triola, J., Barco, R., Moltsen, L., Altman, Z., & Lazaro, P. (2008) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.912610 · Cited by 135
BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-019-01443-z · Cited by 142
Learning Certifiably Optimal Rule Lists for Categorical Data
Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017) · arXiv · doi:10.48550/arxiv.1704.01701 · Cited by 108
Context-aware rule learning from smartphone data: survey, challenges and future directions
Sarker, I. H. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0258-4 · Cited by 105
(2017) · doi:10.1145/3097983.3098047 · Cited by 104
Sarker, I. H., & Kayes, A. S. M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102762 · Cited by 75
Riganello, F., Larroque, S. K., Bahri, M. A., Heine, L., Martial, C., Carrière, M., Charland-Verville, V., Aubinet, C., Vanhaudenhuyse, A., Chatelle, C., et al. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00769 · Cited by 70
Automatic detection of articulation disorders in children with cleft lip and palate
Maier, A., Hönig, F., Bocklet, T., Nöth, E., Stelzle, F., Nkenke, E., & Schuster, M. (2009) · The Journal of the Acoustical Society of America · doi:10.1121/1.3216913 · Cited by 71
Machine Learning Approaches for Early DRG Classification and Resource Allocation
Gartner, D., Kolisch, R., Neill, D. B., & Padman, R. (2015) · INFORMS Journal on Computing · doi:10.1287/ijoc.2015.0655 · Cited by 58
Supersparse Linear Integer Models for Interpretable Classification
Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv · doi:10.48550/arxiv.1306.6677 · Cited by 26
A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning
Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M., & McGovern, A. (2022) · Weather and Forecasting · doi:10.1175/waf-d-22-0070.1 · Cited by 25
“While this does not always mean the simplest ML model available, it does mean that simpler models should be tried before more complicated ones (Holte 1993).”
BackgroundA bias/variance decomposition for models using collective inference
Neville, J., & Jensen, D. (2008) · Machine Learning · doi:10.1007/s10994-008-5066-6 · Cited by 21
Metadata
- Authors
- Holte, R. C.
- Venue
- Machine Learning
- Published
- 1993-04
- Volume
- 11
- Issue
- 1
- Pages
- 63-90
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2132166479
- Cited by
- 1,791
- References
- 2
- FWCI
- 39.50
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.