Handling Different Categories of Concept Drifts in Data Streams using Distributed GP

Created by W.Langdon from gp-bibliography.bib Revision:1.9194

@InProceedings{Folino:2010:EuroGP,

• author = "Gianluigi Folino and Giuseppe Papuzzo",
• title = "Handling Different Categories of Concept Drifts in Data Streams using Distributed {GP}",
• booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010",
• year = "2010",
• editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar",
• volume = "6021",
• series = "LNCS",
• pages = "74--85",
• address = "Istanbul",
• month = "7-9 " # apr,
• organisation = "EvoStar",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-12147-0",
• URL = "https://iris.cnr.it/handle/20.500.14243/70981",
• URL = "https://hdl.handle.net/20.500.14243/70981",
• DOI = "10.1007/978-3-642-12148-7_7",
• abstract = "Using Genetic Programming (GP) for classifying data streams is problematic as GP is slow compared with traditional single solution techniques. However, the availability of cheaper and better-performing distributed and parallel architectures make it possible to deal with complex problems previously hardly solved owing to the large amount of time necessary. This work presents a general framework based on a distributed GP ensemble algorithm for coping with different types of concept drift for the task of classification of large data streams. The framework is able to detect changes in a very efficient way using only a detection function based on the incoming unclassified data. Thus, only if a change is detected a distributed GP algorithm is performed in order to improve classification accuracy and this limits the overhead associated with the use of a population-based method. Real world data streams may present drifts of different types. The introduced detection function, based on the self-similarity fractal dimension, permits to cope in a very short time with the main types of different drifts, as demonstrated by the first experiments performed on some artificial datasets. Furthermore, having an adequate number of resources, distributed GP can handle very frequent concept drifts.",
• notes = "BoostCGPC, cellular GP, island model, AdaBoost, Fractal dimension FD3, cloud computing, Minku

  Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010",

}

Genetic Programming entries for Gianluigi Folino Giuseppe Papuzzo

Citations