The Evolutionary Pre-Processor: Automatic Feature Extraction for Supervised Classification using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Sherrah:1997:epafxsc,

• author = "Jamie R. Sherrah and Robert E. Bogner and Abdesselam Bouzerdoum",
• title = "The Evolutionary Pre-Processor: Automatic Feature Extraction for Supervised Classification using Genetic Programming",
• booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference",
• editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo",
• year = "1997",
• month = "13-16 " # jul,
• keywords = "genetic algorithms, genetic programming",
• pages = "304--312",
• address = "Stanford University, CA, USA",
• publisher_address = "San Francisco, CA, USA",
• publisher = "Morgan Kaufmann",
• URL = "http://citeseer.ist.psu.edu/284782.html",
• abstract = "The extraction of features for classification is often performed heuristically, despite the effect this step has on the performance of the classifier. The Evolutionary Pre-Processor is presented, an automatic nonparametric method for the extraction of non-linear features. Using genetic programming, the Evolutionary Pre-Processor evolves networks of different non-linear functions which pre-process the data to improve the discriminatory performance of a classifier. In experiments performed on 9 real-world data sets, the Evolutionary Pre-Processor was able to pre-process the data to reduce the test set misclassification rate. The dimensionality of the data was decreased and those measurements not required for classification were excised. The Evolutionary PreProcessor behaved intelligently by deciding whether to perform feature extraction or feature selection.",
• notes = "GP-97",

}

Genetic Programming entries for Jamie R Sherrah Robert E Bogner Abdesselam Bouzerdoum

Citations