Multi-Objective Genetic Programming for Dataset Similarity Induction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10317478" target="_blank" >RIV/00216208:11320/15:10317478 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/15:00455776

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7376798" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7376798</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI.2015.222" target="_blank" >10.1109/SSCI.2015.222</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Objective Genetic Programming for Dataset Similarity Induction

Popis výsledku v původním jazyce

Metalearning -- the recommendation of a suitable machine learning technique for a given dataset -- relies on the concept of similarity between datasets. Traditionally, similarity measures have been constructed manually, and thus could not precisely graspthe complex relationship among the different features of the datasets. Recently, we have used an attribute alignment technique combined with genetic programming to obtain more fine-grained and trainable dataset similarity measure. In this paper, we propose an approach based on multi-objective genetic programming for evolving an attribute similarity function. Multi-objective optimization is used to encourage some of the metric properties, thus contributing to the generalization abilities of the similarity function being evolved. Experiments are performed on the data extracted from the OpenML repository and their results are compared to the baseline algorithm.

Název v anglickém jazyce

Multi-Objective Genetic Programming for Dataset Similarity Induction

Popis výsledku anglicky

Metalearning -- the recommendation of a suitable machine learning technique for a given dataset -- relies on the concept of similarity between datasets. Traditionally, similarity measures have been constructed manually, and thus could not precisely graspthe complex relationship among the different features of the datasets. Recently, we have used an attribute alignment technique combined with genetic programming to obtain more fine-grained and trainable dataset similarity measure. In this paper, we propose an approach based on multi-objective genetic programming for evolving an attribute similarity function. Multi-objective optimization is used to encourage some of the metric properties, thus contributing to the generalization abilities of the similarity function being evolved. Experiments are performed on the data extracted from the OpenML repository and their results are compared to the baseline algorithm.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computational Intelligence, 2015 IEEE Symposium Series on

ISBN

978-1-4799-7560-0

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

1576-1582

Název nakladatele

IEEE

Místo vydání

New York, USA

Místo konání akce

Kapské město, Jihoafrická republika

Datum konání akce

7. 12. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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