Improving induction decision trees with parallel genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{folino:2002:euromicro,

• author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano",
• title = "Improving induction decision trees with parallel genetic programming",
• booktitle = "Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing",
• year = "2002",
• pages = "181--187",
• address = "Canary Islands",
• month = "9-11 " # jan,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, data mining, decision trees, learning by example, parallel programming, J-measure, UCI machine learning repository, fitness function, genetic operators, grid model, induction decision trees, large data sets, parallel genetic programming",
• DOI = "doi:10.1109/EMPDP.2002.994264",
• 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",
• notes = "Inspec Accession Number: 7205091",

}

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano

Citations