StreamGP: tracking evolving GP ensembles in distributed data streams using fractal dimension

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1277301,
  author =       "Gianluigi Folino and Clara Pizzuti and 
                 Giandomenico Spezzano",
  title =        "StreamGP: tracking evolving GP ensembles in
                 distributed data streams using fractal dimension",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1751--1751",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1751.pdf",
  DOI =          "doi:10.1145/1276958.1277301",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming: Poster, data
                 mining, distributed streaming data, ensemble",
  abstract =     "The paper presents an adaptive GP boosting ensemble
                 method for the classification of distributed
                 homogeneous streaming data that comes from multiple
                 locations. The approach is able to handle concept drift
                 via change detection by employing a change detection
                 strategy, based on self-similarity of the ensemble
                 behaviour, and measured by its fractal dimension. It is
                 efficient since each node of the network works with its
                 local streaming data, and communicate only the local
                 model computed with the other peer-nodes. Furthermore,
                 once the ensemble has been built, it is used to predict
                 the class membership of new streams of data until
                 concept drift is detected. Only in such a case the
                 algorithm is executed to generate a new set of
                 classifiers to update the current ensemble.
                 Experimental results on a synthetic and real life data
                 set showed the validity of the approach in maintaining
                 an accurate and up-to-date GP ensemble.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano

Citations