Properties of a GP active learning framework for streaming data with class imbalance
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{KhanchiHZ17,
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author = "Sara Khanchi and Malcolm I. Heywood and
A. Nur Zincir-Heywood",
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title = "Properties of a {GP} active learning framework for
streaming data with class imbalance",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "945--952",
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URL = "http://doi.acm.org/10.1145/3071178.3071213",
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DOI = "doi:10.1145/3071178.3071213",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming",
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month = "15-19 " # jul,
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size = "8 pages",
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abstract = "Active learning algorithms attempt to interactively
develop a subset of data from which fitness evaluation
is performed. Moreover, the distribution of labelled
content within the data subset may adapt over time as
genetic programming (GP) individuals improve. The basic
goal is therefore to identify the most meaningful
subset of data to improve the current model. Under a
streaming data context additional challenges exist
relative to the non-streaming scenario: non-stationary
processes, partial observability any time operation.
This means that it is not possible to guarantee that
the content of the data subset even provides exemplars
for each class that could appear in the stream (i.e.,
different classes appear/disappear at different parts
of the stream). With this in mind, an investigation is
performed into the impact of adopting different
policies for controlling the development of data subset
content. To do so, a generic framework is defined in
terms of sampling and archiving policies. The resulting
evaluation under several large multi-class datasets
with class imbalance indicates that adopting random
sampling with a biased archiving policy is sufficient
for evolving GP classifiers that match or better the
current state-of-the-art, particularly when detecting
minor classes.",
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notes = "Also known as \cite{Khanchi:2017:PGA:3071178.3071213,}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Sara Khanchi
Malcolm Heywood
Nur Zincir-Heywood
Citations