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http://hdl.handle.net/20.500.11861/7634
Title: | Data classification using genetic parallel programming |
Authors: | Cheang, Sin Man Lee, Kin Hong Prof. LEUNG Kwong Sak |
Issue Date: | 2003 |
Publisher: | Springer Verlag |
Source: | Genetic and Evolutionary Computation Conference, 2003, pp. 1918 - 1919. |
Conference: | Genetic and Evolutionary Computation Conference |
Abstract: | A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms. © Springer-Verlag Berlin Heidelberg 2003. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/7634 |
ISBN: | 978-354040603-7 |
DOI: | 10.1007/3-540-45110-2_88 |
Appears in Collections: | Publication |
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