- author = "Thomas Loveard and Vic Ciesielski",
- title = "Employing Nominal Attributes in Classification Using Genetic Programming",
- booktitle = "Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)",
- year = "2002",
- editor = "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao",
- pages = "487--491",
- address = "Orchid Country Club, Singapore",
- month = "18-22 " # nov,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "981-04-7522-5",
-
URL = "
http://goanna.cs.rmit.edu.au/~vc/papers/seal02-loveard.pdf",
- size = "5 pages",
-
abstract = "In this paper methods for performing classification
using Genetic Programming (GP) on datasets with nominal
attributes are developed and evaluated. The two methods
developed included the splitting of GP program
execution based upon the value of a nominal attribute
(execution branching), and the conversion of a nominal
attribute to a continuous or binary attribute (numeric
conversion). These two methods of using nominal
attributes are tested against six datasets containing
either nominal and continuous attributes or nominal
only attributes.
Results show that the use of the methods developed in this paper allow classifiers trained with GP to perform accurate classification of datasets containing nominal attributes. When compared to other well-known methods of classification the GP method is capable of classifying one of six datasets more accurately than any of the conventional methods tested, and accuracy close to the best achieved method on 3 other datasets.",
- notes = "SEAL 2002 see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.200.6410&rep=rep1&type=pdf",
