The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A System for Induction of Oblique Decision Trees
Open accessAbstract
This article describes a new system for induction ofoblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a goodoblique split (in the form of a hyperplane) at each node of a decisiontree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We presentextensive empirical studies, using both real and artificial data, thatanalyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examinethe benefits of randomization for the construction of oblique decisiontrees.
Overview
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
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Constructing optimal binary decision trees is NP-complete
Hyafil, L., & Rivest, R. L. (1976) · Information Processing Letters · doi:10.1016/0020-0190(76)90095-8 · Cited by 959
Schaffer, C. (1993) · Machine Learning · doi:10.1023/a:1022653209073 · Cited by 310
Cited by
Decision tree classification of land cover from remotely sensed data
Friedl, M. A., & Brodley, C. E. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00049-7 · Cited by 1,542
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Murthy, S. K. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009744630224 · Cited by 991
Self-citationZheng, R., Li, J., Chen, H., & Huang, Z. (2006) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20316 · Cited by 519
Authentic facial expression analysis
Sebe, N., Lew, M. S., Sun, Y., Cohen, I., Gevers, T., & Huang, T. S. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2005.12.021 · Cited by 257
Metadata
- Authors
- Murthy, S. K., Kasif, S., & Salzberg, S.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1994-08-01
- Volume
- 2
- Pages
- 1-32
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2951539267
- Cited by
- 590
- References
- 4
- FWCI
- 40.35
- Type
- journal article
- Language
- en
- Open access
- diamond
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A System for Induction of Oblique Decision Trees. https://app.cochat.ai/papers/10.1613/jair.63
- The paper
- Murthy, S. K., Kasif, S., & Salzberg, S. (1994). A System for Induction of Oblique Decision Trees. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.63
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.
