A Genetic Algorithm-Based Approach for Building Accurate Decision Trees
By Zhiwei Fu, Bruce L. Golden, Shreevardhan Lele, bullet S Raghavan, Edward A. Wasil, Robert H. Smith. 2003
63 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- André Carlos Ponce de Leon Ferreira de Carvalho
- Rodrigo C. Barros
- Márcio Porto Basgalupp
- Alex Alves Freitas
- Shuning Wu
- Michele Colledanchise
- Petter Ögren
- Cynthia Rudin
In Information Systems
- André Carlos Ponce de Leon Ferreira de Carvalho
- Rodrigo C. Barros
- Márcio Porto Basgalupp
- Alex Alves Freitas
- Hanif D. Sherali
- Shuning Wu
- Chawalit Jeenanunta
- Antoine G. Hobeika
Citations in the corpus
- Optimal instance selection for improved decision tree (2007, methodology): “Fu et al. (2003b) investigated the use of various percentiles of the distribution of classification accuracy, in place of the mean, and developed a genetic algorithm that simultaneously considers two fitness criteria.”
- A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “However, in some EAs, the number of generations is as small as 10 [42], 30 [76], or 50 [41], [43], [44], [49], while in other EAs, it is as large as 5000 [36], [37] or 10 000 [38], [54].”
- An Optimal Constrained Pruning Strategy for Decision Trees (2009, methodology): “Published online in Articles in Advance August 8, 2008.”
- Behavior Trees in Robotics and AI: An Introduction (2017, methodology): “The use of BTs as the knowledge representation framework in the GP avoids the problem of logic violation during cross-over stressed in [19].”