- author = "Jacques-Andre Landry and Luis {Da Costa} and Thomas Bernier",
- title = "Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system",
- journal = "Systemics, Cybernetics and Informatics",
- year = "2006",
- volume = "3",
- number = "1",
- pages = "76--81",
- publisher = "International Institute of Informatics and Cybernetics",
- keywords = "genetic algorithms, genetic programming, Feature selection, pattern recognition",
- ISSN = "1690-4524",
-
URL = "
http://www.iiisci.org/Journal/sci/Abstract.asp?var=&id=P718270",
- size = "6 pages",
- abstract = "In order to implement a multi-class object detection system, an efficient object representation is needed; in this short paper, we present a feature selection method based on the Genetic Programming paradigm. This method allows for the identification of a set of features that best represent the classes in the problem at hand. The idea would then be to have a broad set of features to describe any object, and then to use the presented feature selection method to adapt the description to the actual needs of the classification problem. Furthermore, the tree like solutions generated by the method can be interpreted and modified for increased generality. A brief review of literature, the first implementation of the method and the first results are presented here. The method shows potential to be used as a building block of a detection system, although further experimentation is underway in order to fully asses the power of the method.",
-
notes = "Ecole de Technologie Superieure, Universite du
quebec.
http://www.iiisci.org/Journal/sci/Contents.asp?var=&Previous=ISS1042",
