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10th Euromicro Workshop on Parallel, Distributed and Network-based Processing (EUROMICRO-PDP 2002) (2002)
Canary Islands, Spain
Jan. 9, 2002 to Jan. 11, 2002
ISSN: 1066-6192
ISBN: 0-7695-1444-8
pp: 0181
G. Folino , ISI-CNR
C. Pizzuti , ISI-CNR
G. Spezzano , ISI-CNR
ABSTRACT
A parallel genetic programming approach to induce decision trees in large data sets is presented. A population of trees is evolved by employing the genetic operators and every individual is evaluated by using a fitness function based on the J-measure. The method is able to deal with large data sets since it uses a parallel implementation of genetic programming through the grid model. Experiments on data sets from the UCI machine learning repository show better results with respect to C5. Furthermore, performance results show a nearly linear speedup.
INDEX TERMS
Decision Trees, Genetic programming, classification, parallel processing
CITATION

G. Folino, C. Pizzuti and G. Spezzano, "Improving Induction Decision Trees with Parallel Genetic Programming," 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing (EUROMICRO-PDP 2002)(PDP), Canary Islands, Spain, 2002, pp. 0181.
doi:10.1109/EMPDP.2002.994264
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