Ameva: An autonomous discretization algorithm

作者：Gonzalez Abril L^*; Cuberos F J; Velasco Morente Francisco; Ortega J A

来源：Expert Systems with Applications, 2009, 36(3): 5327-5332.

DOI：10.1016/j.eswa.2008.06.063

• Knowledge discovery
• Supervised discretization
• Machine learning
• Genetic algorithm

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.

• 出版日期2009-4