Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11320/17:10367219

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA15-19877S" target="_blank" >GA15-19877S: Automatické modelování znalostí a plánů pro autonomní roboty</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

International Journal on Artificial Intelligence Tools

ISSN

0218-2130

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

—

Kód UT WoS článku

000413237100006

EID výsledku v databázi Scopus

2-s2.0-85032862100