The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Top-Down Induction of Decision Trees Classifiers—A Survey
Abstract
Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies.
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Citations
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
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
Cited by
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Self-citationCatBoost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09516 · Cited by 1,110
The preeminence of ethnic diversity in scientific collaboration
AlShebli, B. K., Rahwan, T., & Woon, W. L. (2018) · Nature Communications · doi:10.1038/s41467-018-07634-8 · Cited by 490
Adaptive Hypergraph Learning and its Application in Image Classification
Yu, J., Tao, D., & Wang, M. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2190083 · Cited by 405
Series in Machine Perception and Artificial Intelligence
(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 365
Metadata
- Authors
- Rokach, L., & Maimon, O.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
- Published
- 2005-11
- Volume
- 35
- Issue
- 4
- Pages
- 476-487
- ISSN
- 1094-6977 (ISSN-L)
- OpenAlex
- W2148239836
- Cited by
- 792
- References
- 3
- FWCI
- 24.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rokach, L., & Maimon, O. (2005). Top-Down Induction of Decision Trees Classifiers—A Survey. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). https://doi.org/10.1109/tsmcc.2004.843247
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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.
