Multi-objective techniques in genetic programming for evolving classifiers
- Resource Type
- Conference
- Authors
- Parrott, D.; Xiaodong Li; Ciesielski, V.
- Source
- 2005 IEEE Congress on Evolutionary Computation Evolutionary Computation Evolutionary Computation, 2005. The 2005 IEEE Congress on. 2:1141-1148 Vol. 2 2005
- Subject
- Computing and Processing
Genetic programming
Computational efficiency
Evolutionary computation
Error analysis
Diversity reception
Computer science
Information technology
Australia
Application software
Breast cancer
- Language
- ISSN
- 1089-778X
1941-0026
The application of multi-objective evolutionary computation techniques to the genetic programming of classifiers has the potential to both improve the accuracy and decrease the training time of the classifiers. The performance of two such algorithms is investigated on the even 6-parity problem and the Wisconsin breast cancer, Iris and Wine data sets from the UCI repository. The first method explores the addition of an explicit size objective as a parsimony enforcement technique. The second represents a program's classification accuracy on each class as a separate objective. Both techniques give a lower error rate with less computational cost than was achieved using a standard GP with the same parameters.