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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F05%3A03107899" target="_blank" >RIV/68407700:21230/05:03107899 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Inducing Diverse Decision Forests with Genetic Programming

Popis výsledku v původním jazyce

This paper presents an algorithm for induction of ensembles of decision trees, also referred to as decision forests. In order to achieve high expressiveness the trees induced are multivariate, with various, possibly user-defined tests in their internal nodes. Strongly typed genetic programming is utilized to evolve structure of the tests. Special attention is given to the problem of diversity of the forest constructed. An approach is proposed, which explicitly encourages the induction algorithm to produce a different tree each run, which represents an alternative description of the data. It is shown that forests constructed this way have significantly reduced classification error even for small forest size, compared to other ensemble methods. Classification accuracy is also compared to other recent methods on several real-world datasets.

Název v anglickém jazyce

Inducing Diverse Decision Forests with Genetic Programming

Popis výsledku anglicky

This paper presents an algorithm for induction of ensembles of decision trees, also referred to as decision forests. In order to achieve high expressiveness the trees induced are multivariate, with various, possibly user-defined tests in their internal nodes. Strongly typed genetic programming is utilized to evolve structure of the tests. Special attention is given to the problem of diversity of the forest constructed. An approach is proposed, which explicitly encourages the induction algorithm to produce a different tree each run, which represents an alternative description of the data. It is shown that forests constructed this way have significantly reduced classification error even for small forest size, compared to other ensemble methods. Classification accuracy is also compared to other recent methods on several real-world datasets.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/1ET201210527" target="_blank" >1ET201210527: Znalostní podpora diagnostiky a predikce v kardiologii</a><br>

Návaznosti

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

Rok uplatnění

2005

Kód důvěrnosti údajů

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

Název statě ve sborníku

Genetic Programming

ISBN

3-540-25436-6

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

301-310

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Lausanne

Datum konání akce

30. 3. 2005

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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