Testing of Inductive Preprocessing Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00159932" target="_blank" >RIV/68407700:21230/09:00159932 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/09:00159932

Výsledek na webu

<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.1155&rep=rep1&type=pdf" target="_blank" >http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.1155&rep=rep1&type=pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Testing of Inductive Preprocessing Algorithm

Popis výsledku v původním jazyce

The data preprocessing is very important part of the knowledge discovery process. Data mining systems contains tens of preprocessing methods (for example methods for missing data imputation, data reduction, discretization, data enrichment, etc...) and usually it is not clear which methods to use. The selection of preprocessing methods appropriate for particular dataset needs strong experience and a lot of experimenting. In this paper we will test our extension of inductive approach to data preprocessing. We developed inductive preprocessing method which utilizes genetic algorithm to compose from scratch a sequence of preprocessing methods which fits to the data and allows successful model to be created. To test our automatic preprocessing utilize several real-world datasets available from UCI Machine learning repository.

Název v anglickém jazyce

Testing of Inductive Preprocessing Algorithm

Popis výsledku anglicky

The data preprocessing is very important part of the knowledge discovery process. Data mining systems contains tens of preprocessing methods (for example methods for missing data imputation, data reduction, discretization, data enrichment, etc...) and usually it is not clear which methods to use. The selection of preprocessing methods appropriate for particular dataset needs strong experience and a lot of experimenting. In this paper we will test our extension of inductive approach to data preprocessing. We developed inductive preprocessing method which utilizes genetic algorithm to compose from scratch a sequence of preprocessing methods which fits to the data and allows successful model to be created. To test our automatic preprocessing utilize several real-world datasets available from UCI Machine learning repository.

Druh

O - Ostatní výsledky

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/KJB201210701" target="_blank" >KJB201210701: Automatická extrakce znalostí</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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