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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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Citations
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
Witten, I. H. (2008) · Cited by 9,143
Cohen, W. W. (1995) · Machine Learning Proceedings 1995 · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,787
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870
Alcalá‐Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., & Herrera, F. (2011) · Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte) · Cited by 2,328
Self-citationGarcía, S., Fernández, A., Luengo, J., & Herrera, F. (2010) · Information Sciences · doi:10.1016/j.ins.2009.12.010 · Cited by 2,229
Self-citationSupervised and Unsupervised Discretization of Continuous Features
Dougherty, J., Kohavi, R., & Sahami, M. (1995) · Machine Learning Proceedings 1995 · doi:10.1016/b978-1-55860-377-6.50032-3 · Cited by 1,936
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
Holte, R. C. (1993) · Machine Learning · doi:10.1023/a:1022631118932 · Cited by 1,791
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
SUN, Y., WONG, A. K. C., & KAMEL, M. S. (2009) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001409007326 · Cited by 1,692
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M. J., Ventura, S., Garrell, J. M., Otero, J., Romero, C., Bacardit, J., Rivas, V. M., et al. (2009) · Soft Computing · doi:10.1007/s00500-008-0323-y · Cited by 1,505
Self-citationData preparation for data mining
Pyle, D. (1999) · Cited by 1,368
García, S., & Herrera, F. (2008) · Cited by 1,277
Self-citationReduction Techniques for Instance-Based Learning Algorithms
Wilson, D. R., & Martinez, T. R. (2000) · Machine Learning · doi:10.1023/a:1007626913721 · Cited by 1,182
Data mining and knowledge discovery handbook
(2011) · Choice Reviews Online · doi:10.5860/choice.48-5729 · Cited by 1,156
Statistical Comparisons of Classifiers over Multiple Data Sets
DemšarJanez (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248548 · Cited by 1,074
Discretization: An Enabling Technique
Liu, H., Hussain, F., Tan, C. L., & Dash, M. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1016304305535 · Cited by 981
The Top Ten Algorithms in Data Mining
(2009) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420089653 · Cited by 670
ChiMerge: discretization of numeric attributes
Kerber, R. (1992) · National Conference on Artificial Intelligence · Cited by 657
(2007) · doi:10.1007/978-0-387-36795-8 · Cited by 613
Kurgan, L. A., & Cios, K. J. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.1269594 · Cited by 443
On changing continuous attributes into ordered discrete attributes
Catlett, J. (1991) · Lecture Notes in Computer Science · doi:10.1007/bfb0017012 · Cited by 426
Global discretization of continuous attributes as preprocessing for machine learning
Chmielewski, M. R., & Grzymala-Busse, J. W. (1996) · International Journal of Approximate Reasoning · doi:10.1016/s0888-613x(96)00074-6 · Cited by 347
Feature selection via discretization
Liu, H., & Setiono, R. (1997) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.617056 · Cited by 325
Machine learning and data mining
Kononenko, I., & Kukar, M. (2007) · Woodhead Publishing Limited eBooks · doi:10.1533/9780857099440 · Cited by 293
Discretization for naive-Bayes learning: managing discretization bias and variance
Yang, Y., & Webb, G. I. (2009) · Machine Learning · doi:10.1007/s10994-008-5083-5 · Cited by 250
Optimal partitioning for classification and regression trees
Chou, P. A. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.88569 · Cited by 248
Class-dependent discretization for inductive learning from continuous and mixed-mode data
Ching, J. Y., Wong, A. K. C., & Chan, K. C. C. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.391407 · Cited by 241
Machine learning and data mining: introduction to principles and algorithms
(2008) · Choice Reviews Online · doi:10.5860/choice.45-3834 · Cited by 233
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
Rastogi, R., & Shim, K. (2000) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009887311454 · Cited by 216
Gaddam, S. R., Phoha, V. V., & Balagani, K. S. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.44 · Cited by 210
A discretization algorithm based on Class-Attribute Contingency Coefficient
Tsai, C.-J., Lee, C.-I., & Yang, W.-P. (2008) · Information Sciences · doi:10.1016/j.ins.2007.09.004 · Cited by 202
Discretizing continuous attributes while learning Bayesian networks
Friedman, N., & Goldszmidt, M. (1996) · Cited by 196
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
Wong, A. K. C., & Chiu, D. K. Y. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767986 · Cited by 192
General and Efficient Multisplitting of Numerical Attributes
Elomaa, T., & Rousu, J. (1999) · Machine Learning · doi:10.1023/a:1007674919412 · Cited by 164
MODL: A Bayes optimal discretization method for continuous attributes
Boullé, M. (2006) · Machine Learning · doi:10.1007/s10994-006-8364-x · Cited by 159
An extended Chi2 algorithm for discretization of real value attributes
Su, C., & Hsu, J.-H. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.39 · Cited by 158
A modified Chi2 algorithm for discretization
Tay, F. E. H., & Shen, L. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2002.1000349 · Cited by 154
A discretization algorithm based on a heterogeneity criterion
Liu, X., & Wang, H. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.135 · Cited by 144
Use of contextual information for feature ranking and discretization
Hong, S. J. (1997) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.634751 · Cited by 127
A Survey on Graphical Methods for Classification Predictive Performance Evaluation
Prati, R. C., Batista, G., & Monard, M. C. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.59 · Cited by 113
A lot of randomness is hiding in accuracy
Ben‐David, A. (2007) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2007.01.001 · Cited by 111
Khiops: A Statistical Discretization Method of Continuous Attributes
Boullé, M. (2004) · Machine Learning · doi:10.1023/b:mach.0000019804.29836.05 · Cited by 111
Ameva: An autonomous discretization algorithm
Abril, L. G., Cuberos, F. J., Morente, F. V., & Ortega, J. A. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.063 · Cited by 93
Fuzzy discretization of feature space for a rough set classifier
Roy, A., & Pal, S. K. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00201-5 · Cited by 76
Data discretization unification
Jin, R., Breitbart, Y. J., & Muoh, C. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-008-0142-6 · Cited by 74
FUSINTER: A Method for Discretization of Continuous Attributes
Zighed, D. A., Rabaséda, S., & Rakotomalala, R. (1998) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488598000264 · Cited by 72
Yang, Y., Webb, G. I., & Wu, X. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_6 · Cited by 70
A clustering-based discretization for supervised learning
Gupta, A., Mehrotra, K. G., & Mohan, C. K. (2010) · Statistics & Probability Letters · doi:10.1016/j.spl.2010.01.015 · Cited by 61
On Finding Optimal Discretizations for Two Attributes
Chlebus, B. S., & Nguyen, S. H. (1998) · Lecture notes in computer science · doi:10.1007/3-540-69115-4_74 · Cited by 58
Discretization of Continuous Attributes for Learning Classification Rules
An, A., & Cercone, N. J. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48912-6_69 · Cited by 53
Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates
Elomaa, T., & Rousu, J. (2004) · Data Mining and Knowledge Discovery · doi:10.1023/b:dami.0000015868.85039.e6 · Cited by 45
A greedy algorithm for supervised discretization
Butterworth, R., Simovici, D. A., dos Santos, G. S., & Lucila Ohno-machado, A. (2004) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2004.07.006 · Cited by 40
Janssens, D., Brijs, T., Vanhoof, K., & Wets, G. (2005) · Computers & Operations Research · doi:10.1016/j.cor.2005.01.022 · Cited by 40
Highly scalable and robust rule learner: performance evaluation and comparison
Kurgan, L., Cios, K. J., & Dick, S. (2006) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.852983 · Cited by 40
Flores, M. J., Gámez, J. A., Martínez, A. M., & Puerta, J. M. (2011) · Applied Intelligence · doi:10.1007/s10489-011-0286-z · Cited by 37
A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label
Hu, H.-W., Chen, Y., & Tang, K. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.24 · Cited by 37
Discretization for Continuous Attributes
Muhlenbach, F., & Rakotomalala, R. (2005) · IGI Global eBooks · doi:10.4018/978-1-59140-557-3.ch076 · Cited by 27
A latent variable model for multivariate discretization.
Monti, S., & Cooper, G. F. (1999) · Cited by 22
A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches
Wu, Q., Bell, D., Prasad, G., & McGinnity, T. M. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.250582 · Cited by 17
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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
Self-citationGong, H., Li, Y., Zhang, J., Zhang, B., & Wang, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107865 · Cited by 331
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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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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