Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00481656" target="_blank" >RIV/67985807:_____/17:00481656 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/17:10367219

Result on the web

<a href="http://dx.doi.org/10.1142/S021821301760020X" target="_blank" >http://dx.doi.org/10.1142/S021821301760020X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S021821301760020X" target="_blank" >10.1142/S021821301760020X</a>

Result language

angličtina

Original language name

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Original language description

Manual creation of machine learning ensembles is a hard and tedious task which requires an expert and a lot of time. In this work we describe a new version of the GP-ML algorithm which uses genetic programming to create machine learning workflows (combinations of preprocessing, classification, and ensembles) automatically, using strongly typed genetic programming and asynchronous evolution. The current version improves the way in which the individuals in the genetic programming are created and allows for much larger workflows. Additionally, we added new machine learning methods. The algorithm is compared to the grid search of the base methods and to its previous versions on a set of problems from the UCI machine learning repository.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal on Artificial Intelligence Tools

ISSN

0218-2130

e-ISSN

—

Volume of the periodical

26

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

24

Pages from-to

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UT code for WoS article

000413237100006

EID of the result in the Scopus database

2-s2.0-85032862100