Preprocessing input data for machine learning by FCA
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Preprocessing input data for machine learning by FCA
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GPP202%2F10%2FP360" target="_blank" >GPP202/10/P360: Classification with use of formal concept analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
12
Pages from-to
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Publisher name
University of Sevilla
Place of publication
Sevilla
Event location
Sevilla, Španělsko
Event date
Oct 19, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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