C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993
Open accessThe 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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Traditional machine learning techniques such as k-nearest neighbors, decision trees, support vector machines, extreme learning machines, and Gaussian process regression are effective in SOC estimation with limited computational resources.
“Traditional ML techniques, such as k-nearest neighbors (KNNs)[10], decision trees[11], support vector machines (SVMs)[12], extreme learning machines (ELMs)[13], and Gaussian process regression (GPR), are effective in SOC estimation with limited computational resources.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [11]
The Summary of contents describes decision trees as widely used classifiers and says C4.5 trees are small and accurate in several domains, yielding fast, reliable classification. It does not address battery state-of-charge (SOC) estimation or establish effectiveness under limited computational resources in that setting. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
An Empirical Comparison of Pruning Methods for Decision Tree Induction
Mingers, J. (1989) · Machine Learning · doi:10.1023/a:1022604100933 · Cited by 628 · see citing works
UNKNOWN ATTRIBUTE VALUES IN INDUCTION
Quinlan, J. R. (1989) · Elsevier eBooks · doi:10.1016/b978-1-55860-036-2.50048-5 · Cited by 302 · see citing works
Quinlan, J. R. (1993) · Elsevier eBooks · doi:10.1016/b978-0-08-050058-4.50008-5 · Cited by 26 · see citing works
Deconstructing the Digit Recognition Problem
Schaffer, C. (1992) · Elsevier eBooks · doi:10.1016/b978-1-55860-247-2.50056-5 · Cited by 17 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507 · see citing works
Voice Disorder Identification by Using Machine Learning Techniques
Verde, L., De Pietro, G., & Sannino, G. (2018) · IEEE Access · doi:10.1109/access.2018.2816338 · Cited by 156 · see citing works
A review on machine learning approaches for microalgae cultivation systems
Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92 · see citing works
Fan, D., Yang, H., Li, F., Sun, L., Di, P., Li, W., Tang, Y., & Liu, G. (2018) · Toxicology Research · doi:10.1039/c7tx00259a · Cited by 70 · see citing works
Malik, M. N., Azam, M. A., Ehatisham-Ul-Haq, M., Ejaz, W., & Khalid, A. (2019) · Sensors · doi:10.3390/s19112466 · Cited by 39 · see citing works
Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20 · see citing works
- Authors
- Salzberg, S. L.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Salzberg, Steven L.
- ORCID
- Salzberg, Steven L.
- Venue
- Machine Learning
- Published
- 1994-09
- Volume
- 16
- Issue
- 3
- Pages
- 235-240
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2089367555
- Cited by
- 746
- References
- 8
- Citations by year
- 2026: 24 · 2025: 53 · 2024: 81 · 2023: 88 · 2022: 106 · 2021: 90 · 2020: 71 · 2019: 59 · 2018: 61 · 2017: 39 · 2016: 24 · 2015: 21 · 2014: 11 · 2013: 4 · 2012: 6
- Type
- book review
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- C4.5, decision tree induction, machine learning software, ID3
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
