Exploiting fractal dimension and a distributed evolutionary approach to classify data streams with concept drifts
Created by W.Langdon from
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- @Article{FOLINO:2019:ASC,
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author = "Gianluigi Folino and Massimo Guarascio and
Giuseppe Papuzzo",
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title = "Exploiting fractal dimension and a distributed
evolutionary approach to classify data streams with
concept drifts",
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journal = "Applied Soft Computing",
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volume = "75",
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pages = "284--297",
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year = "2019",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2018.11.009",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494618306410",
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abstract = "Evolutionary algorithms, i.e., Genetic Programming
(GP), have been successfully used for the task of
classification, mainly because they are less likely to
get stuck in the local optimum, can operate on chunks
of data and allow to compute more solutions in
parallel. Ensemble techniques are usually more accurate
than component learners constituting the ensemble and
can be built in an incremental way, improving
flexibility, adapting to changes and maintaining part
of the history present in the data. This paper proposes
a 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 fractal dimension, which can also works on
new incoming unclassified data. Thus, a distributed GP
algorithm is performed only when a change is detected
in order to improve classification accuracy and this,
together with the exploitation of an adaptive
procedure, permits to answer in short time to these
changes. Experiments are conducted on a real and on
some artificial datasets in order to assess the
capacity of the framework to detect the drift and
quickly respond to it",
- }
Genetic Programming entries for
Gianluigi Folino
Massimo Guarascio
Giuseppe Papuzzo
Citations