The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
CART: Classification and Regression Trees
Abstract
The 1984 monograph, “CART: Classification and Regression Trees,” coauthored by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone (BFOS), represents a major milestone in the evolution of artificial intelligence, machine learning, nonparametric statistics, and data mining. The work is important for the comprehensiveness of its study of decision trees, the technical innovations it introduces, its sophisticated examples of tree-structured data analysis, and its authoritative treatment of large sample theory for trees. Since its publication the CART monograph has been cited some 3000 times according to the science and social science citation indexes; Google Scholar reports about 8,450 citations. CART citations can be found in almost any domain, with many appearing in fields such as credit risk, targeted marketing, financial markets modeling, electrical engineering, quality control, biology, chemistry, and clinical medical research. CART has also strongly influenced image compression via tree-structured vector quantization. This brief account is intended to introduce CART basics, touching on the major themes treated in the CART monograph, and to encourage readers to return to the rich original source for technical details, discussions revealing the thought processes of the authors, and examples of their analytical style.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
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Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Self-citationNeural Networks and the Bias/Variance Dilemma
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Cited by
Pyšek, P., Jarošík, V., Hulme, P. E., Pergl, J., Hejda, M., Schaffner, U., & Vilà, M. (2012) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02636.x · Cited by 1,447
Multiple additive regression trees with application in epidemiology
Friedman, J. H., & Meulman, J. J. (2003) · Statistics in Medicine · doi:10.1002/sim.1501 · Cited by 786
Decision tree and random forest implementations for fast filtering of sensor data
Buschjager, S., & Morik, K. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2017.2710627 · Cited by 78
Metadata
- Venue
- The Top Ten Algorithms in Data Mining
- Publisher
- Chapman and Hall/CRC
- Published
- 2009-04-09
- Pages
- 193-216
- OpenAlex
- W2968627562
- Cited by
- 163
- References
- 14
- FWCI
- 31.02
- Type
- book chapter
- Language
- en
- Open access
- closed
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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.
