Comprehensible credit scoring models using rule extraction from support vector machines
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?
Pedagogical techniques directly extract rules relating inputs and outputs of the predictor, while decompositional approaches are closely intertwined with the internal structure of the SVM.
“Pedagogical techniques f directly extract rules which relate the inputs and outputs of the predictor (e.g. [20,37]), while decompositional approaches are closely intertwined with the internal structure of the SVM (e.g. [70,28]).”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [65]
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
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286 · see citing works
Machine learning in manufacturing: Advantages, challenges, and applications
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016) · Production & Manufacturing Research · doi:10.1080/21693277.2016.1192517 · Cited by 1,319 · see citing works
Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284 · see citing works
Self-citationExplainable artificial intelligence: A survey
Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) · doi:10.23919/mipro.2018.8400040 · Cited by 1,147 · see citing works
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 · see citing works
Consumer credit-risk models via machine-learning algorithms
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.06.001 · Cited by 864 · see citing works
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · see citing works
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559 · see citing works
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.12.003 · Cited by 425 · see citing works
Self-citationExplaining Data-Driven Document Classifications1
Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264 · see citing works
Self-citationA Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
“In Reference [76] the authors propose a qualitative comparison of the explanations returned by techniques for extraction of rules from SVM black boxes (e.”
BackgroundMethodInfluentialInterpretable machine learning for imbalanced credit scoring datasets
Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.036 · Cited by 190 · see citing works
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works
Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection?
Papenkordt, J., Ngonga Ngomo, A.-C., & Thommes, K. (2026) · Behaviour & Information Technology · doi:10.1080/0144929x.2025.2568928 · Cited by 2 · see citing works
“Although non-expert users have difficulty understanding probabilities (Gigerenzer et al. 2005; Martens et al. 2007), our results suggest that numerical prediction certainties are a suitable signal for end-users by reflecting and quantifying the uncertainty in AI recommendations (Gneiting and…”
- Authors
- Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J.
- Affiliations
- KU Leuven (BE)
- University of Southampton (GB)
- Corresponding author
- Martens, David
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2007-12
- Volume
- 183
- Issue
- 3
- Pages
- 1466-1476
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W3123427206
- Cited by
- 448
- References
- 1
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 15 · 2023: 18 · 2022: 22 · 2021: 29 · 2020: 28 · 2019: 30 · 2018: 27 · 2017: 23 · 2016: 36 · 2015: 27 · 2014: 25 · 2013: 23 · 2012: 16
- FWCI
- 29.80
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- European Journal of Operational Research (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Lirias (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Keywords
- support vector machine, rule extraction, interpretable classification, credit scoring, credit risk assessment, trepan, explainable AI, diagnosis, comprehensibility
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
