Towards a more efficient representation of imputation operators in TPOT

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Misc{DBLP:journals/corr/abs-1801-04407,

• author = "Unai Garciarena and Alexander Mendiburu and Roberto Santana",
• title = "Towards a more efficient representation of imputation operators in {TPOT}",
• howpublished = "arXiv",
• year = "2018",
• month = "13 " # jan,
• keywords = "genetic algorithms, genetic programming, TPOT, STGP, missing data, imputation methods, supervised classification, automatic machine learning, sklearn pipelines",
• eprint = "1801.04407",
• biburl = "https://dblp.org/rec/journals/corr/abs-1801-04407.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "http://arxiv.org/abs/1801.04407",
• size = "13 pages",
• 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