AVATAR Machine Learning Pipeline Evaluation Using Surrogate Model
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Nguyen:2020:IDA,
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author = "Tien-Dung Nguyen and Tomasz Maszczyk and
Katarzyna Musial and Marc-Andre Zoeller and Bogdan Gabrys",
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title = "{AVATAR} Machine Learning Pipeline Evaluation Using
Surrogate Model",
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booktitle = "IDA 2020: Advances in Intelligent Data Analysis
XVIII",
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year = "2020",
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editor = "Michael R. Berthold and Ad Feelders and Georg Krempl",
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volume = "12080",
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series = "Lecture Notes in Computer Science",
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pages = "352--365",
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address = "Konstanz, Germany",
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month = apr # " 27-29",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, TPOT",
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isbn13 = "978-3-030-44583-6",
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DOI = "doi:10.1007/978-3-030-44584-3_28",
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abstract = "The evaluation of machine learning (ML) pipelines is
essential during automatic ML pipeline composition and
optimisation. The previous methods such as
Bayesian-based and genetic-based optimisation, which
are implemented in Auto-Weka, Auto-sklearn and TPOT,
evaluate pipelines by executing them. Therefore, the
pipeline composition and optimisation of these methods
requires a tremendous amount of time that prevents them
from exploring complex pipelines to find better
predictive models. To further explore this research
challenge, we have conducted experiments showing that
many of the generated pipelines are invalid, and it is
unnecessary to execute them to find out whether they
are good pipelines. To address this issue, we propose a
novel method to evaluate the validity of ML pipelines
using a surrogate model (AVATAR). The AVATAR enables to
accelerate automatic ML pipeline composition and
optimisation by quickly ignoring invalid pipelines. Our
experiments show that the AVATAR is more efficient in
evaluating complex pipelines in comparison with the
traditional evaluation approaches requiring their
execution.",
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notes = "University of Technology Sydney Australia",
- }
Genetic Programming entries for
Tien-Dung Nguyen
Tomasz Maszczyk
Katarzyna Musial
Marc-Andre Zoeller
Bogdan Gabrys
Citations