Name
Abstract | ||
|---|---|---|
This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a potentially minimal number of discrete intervals. Its most important advantage, in contrast with several existing discretization algorithms, is that it does not need the user to indicate the number of intervals. We have compared Ameva with one of the most relevant discretization algorithms, CAIM. Tests performed comparing these two algorithms show that discrete attributes generated by the Ameva algorithm always have the lowest number of intervals, and even if the number of classes is high, the same computational complexity is maintained. A comparison between the Ameva and the genetic algorithm approaches has been also realized and there are very small differences between these iterative and combinatorial approaches, except when considering the execution time. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.06.063 | Expert Syst. Appl. |
Keywords | Field | DocType |
genetic algorithm approach,supervised learning algorithm,knowledge discovery,ameva algorithm,minimal number,discrete attribute,genetic algorithm,existing discretization algorithm,autonomous discretization algorithm,new discretization algorithm,discrete interval,lowest number,relevant discretization algorithm,machine learning,supervised discretization,supervised learning,computational complexity | Discretization,Computer science,Supervised training,Artificial intelligence,Genetic algorithm,Mathematical optimization,Algorithm,Contingency table,Knowledge extraction,Execution time,Machine learning,Computational complexity theory,Discretization of continuous features | Journal |
Volume | Issue | ISSN |
36 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
35 | 1.04 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Gonzalez-Abril | 1 | 153 | 8.48 |
| F. J. Cuberos | 2 | 40 | 1.83 |
| F. Velasco | 3 | 106 | 5.83 |
| J. A. Ortega | 4 | 99 | 7.03 |