Towards a more efficient representation of imputation operators in TPOT
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Misc{DBLP:journals/corr/abs-1801-04407,
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author = "Unai Garciarena and Alexander Mendiburu and
Roberto Santana",
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title = "Towards a more efficient representation of imputation
operators in {TPOT}",
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howpublished = "arXiv",
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year = "2018",
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month = "13 " # jan,
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keywords = "genetic algorithms, genetic programming, TPOT, STGP,
missing data, imputation methods, supervised
classification, automatic machine learning, sklearn
pipelines",
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eprint = "1801.04407",
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biburl = "https://dblp.org/rec/journals/corr/abs-1801-04407.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "http://arxiv.org/abs/1801.04407",
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size = "13 pages",
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abstract = "Automated Machine Learning encompasses a set of
meta-algorithms intended to design and apply machine
learning techniques (e.g., model selection,
hyper-parameter tuning, model assessment, etc.). TPOT,
a software for optimizing machine learning pipelines
based on genetic programming (GP), is a novel example
of this kind of applications. Recently we have proposed
a way to introduce imputation methods as part of TPOT.
While our approach was able to deal with problems with
missing data, it can produce a high number of
unfeasible pipelines. We propose a strongly-typed-GP
based approach that enforces constraint satisfaction by
GP solutions. The enhancement we introduce is based on
the redefinition of the operators and implicit
enforcement of constraints in the generation of the GP
trees. We evaluate the method to introduce imputation
methods as part of TPOT. We show that the method can
notably increase the efficiency of the GP search for
optimal pipelines.",
- }
Genetic Programming entries for
Unai Garciarena Hualde
Alexander Mendiburu
Roberto Santana
Citations