Model Selection and Overfitting in Genetic Programming: Empirical Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00231779" target="_blank" >RIV/68407700:21230/15:00231779 - isvavai.cz</a>

Výsledek na webu

<a href="http://dl.acm.org/citation.cfm?id=2764678&CFID=715756301&CFTOKEN=65340477" target="_blank" >http://dl.acm.org/citation.cfm?id=2764678&CFID=715756301&CFTOKEN=65340477</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2739482.2764678" target="_blank" >10.1145/2739482.2764678</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model Selection and Overfitting in Genetic Programming: Empirical Study

Popis výsledku v původním jazyce

Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the problem of overfitting which cannot be solved or suppresed as easily as in more traditional approaches. Another problem, closely related to overfitting, is the selection of the final model from the population. In this article we present our research that addresses both problems: overfitting and model selection. We compare severalways of dealing with ovefitting, based on Random Sampling Technique (RST) and on using a validation set, all with an emphasis on model selection. We subject each approach to a thorough testing on artificial and real?world datasets and compare them with the standard approach, which uses the full training data, as a baseline.

Název v anglickém jazyce

Model Selection and Overfitting in Genetic Programming: Empirical Study

Popis výsledku anglicky

Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the problem of overfitting which cannot be solved or suppresed as easily as in more traditional approaches. Another problem, closely related to overfitting, is the selection of the final model from the population. In this article we present our research that addresses both problems: overfitting and model selection. We compare severalways of dealing with ovefitting, based on Random Sampling Technique (RST) and on using a validation set, all with an emphasis on model selection. We subject each approach to a thorough testing on artificial and real?world datasets and compare them with the standard approach, which uses the full training data, as a baseline.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (GECCO 2015)

ISBN

978-1-4503-3488-4

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

1527-1528

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Madrid

Datum konání akce

11. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—