The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
A Survey of Evolutionary Algorithms for Decision-Tree Induction
Abstract
This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.
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Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
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 23,522
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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 20,825
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
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Metadata
- Authors
- Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., & Freitas, A. A.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
- Published
- 2012-05
- Volume
- 42
- Issue
- 3
- Pages
- 291-312
- ISSN
- 1094-6977 (ISSN-L)
- 1558-2442
- OpenAlex
- W2148423395
- Cited by
- 346
- References
- 83
- FWCI
- 17.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., & Freitas, A. A. (2012). A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). https://doi.org/10.1109/tsmcc.2011.2157494
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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.
