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

Published: 07 July 2007 Publication History

Abstract

The paper presents an adaptive GP boosting ensemble method forthe 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 behavior, and measured by its fractal dimension. It is efficient since each nodeof the network works with its local streaming data, and communicate only the local model computed with the otherpeer-nodes. Furthermore, once the ensemble has been built, it isused 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 reallife data set showed the validity of the approach in maintaining an accurate and up-to-date GP ensemble.

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  • (2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023

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

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      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 July 2007

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      Author Tags

      1. classification
      2. data mining
      3. distributed streaming data
      4. ensemble
      5. genetic programming

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      GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      • (2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023

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