Preprocessing input data for machine learning by FCA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F10%3A10216519" target="_blank" >RIV/61989592:15310/10:10216519 - 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

Preprocessing input data for machine learning by FCA

Popis výsledku v původním jazyce

The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets.

Název v anglickém jazyce

Preprocessing input data for machine learning by FCA

Popis výsledku anglicky

The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GPP202%2F10%2FP360" target="_blank" >GPP202/10/P360: Klasifikace s využitím formální konceptuální analýzy</a><br>

Návaznosti

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

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 7th International Conference on Concept Lattices and Their Applications

ISBN

978-84-614-4027-6

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

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Název nakladatele

University of Sevilla

Místo vydání

Sevilla

Místo konání akce

Sevilla, Španělsko

Datum konání akce

19. 10. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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