The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning
Abstract
Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. In this manner, symbolic data mining algorithms can be applied over continuous data and the representation of information is simplified, making it more concise and specific. The literature provides numerous proposals of discretization and some attempts to categorize them into a taxonomy can be found. However, in previous papers, there is a lack of consensus in the definition of the properties and no formal categorization has been established yet, which may be confusing for practitioners. Furthermore, only a small set of discretizers have been widely considered, while many other methods have gone unnoticed. With the intention of alleviating these problems, this paper provides a survey of discretization methods proposed in the literature from a theoretical and empirical perspective. From the theoretical perspective, we develop a taxonomy based on the main properties pointed out in previous research, unifying the notation and including all the known methods up to date. Empirically, we conduct an experimental study in supervised classification involving the most representative and newest discretizers, different types of classifiers, and a large number of data sets. The results of their performances measured in terms of accuracy, number of intervals, and inconsistency have been verified by means of nonparametric statistical tests. Additionally, a set of discretizers are highlighted as the best performing ones.
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Cites
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,155
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
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
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Cited by
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García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
Self-citationA review of microarray datasets and applied feature selection methods
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.05.042 · Cited by 666
Self-citationSelf-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Triguero, I., García, S., & Herrera, F. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-013-0706-y · Cited by 573
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Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
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Triguero, I., González, S., Moyano, J. M., García, S., Alcalá-Fdez, J., Luengo, J., Fernández, A., del Jesús, M. J., Sánchez, L., & Herrera, F. (2017) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.10.1.82 · Cited by 286
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Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
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Song, H., Triguero, I., & Özcan, E. (2019) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00185-z · Cited by 101
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Metadata
- Authors
- Garcia, S., Luengo, J., Sáez, J. A., López, V., & Herrera, F.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2013-04
- Volume
- 25
- Issue
- 4
- Pages
- 734-750
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2097994458
- Cited by
- 548
- References
- 68
- FWCI
- 68.15
- Type
- journal article
- Language
- en
- Open access
- closed
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