Mining Distributed Evolving Data Streams using Fractal GP Ensembles

Created by W.Langdon from gp-bibliography.bib Revision:1.7778

@InProceedings{eurogp07:folino,

• author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano",
• title = "Mining Distributed Evolving Data Streams using Fractal GP Ensembles",
• editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
• booktitle = "Proceedings of the 10th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "4445",
• year = "2007",
• address = "Valencia, Spain",
• month = "11-13 " # apr,
• pages = "160--169",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-71602-5",
• isbn13 = "978-3-540-71602-0",
• DOI = "doi:10.1007/978-3-540-71605-1_15",
• abstract = "A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is its adaptability in presence of concept drift. Changes in data can cause serious deterioration of the ensemble performance. Our approach is able to discover changes by adopting a strategy based on self-similarity of the ensemble behaviour, measured by its fractal dimension, and to revise itself by promptly restoring classification accuracy. Experimental results on a synthetic data set show the validity of the approach in maintaining an accurate and up-to-date GP ensemble.",
• notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007",

}

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano

Citations