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Boolean factor analysis for data preprocessing in machine learning

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F10%3A10216523" target="_blank" >RIV/61989592:15310/10:10216523 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Boolean factor analysis for data preprocessing in machine learning

  • Original language description

    We present two input data preprocessing methods for machine learning (ML). 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. The methods utilize formal concept analysis (FCA) and boolean factor analysis, recently described by FCA, in that the new attributes are defined by so-called factor concepts computed from input data table. The methods are demonstrated on 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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

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 ICMLA 2010

  • ISBN

    978-0-7695-4300-0

  • ISSN

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

  • Publisher name

    IEEE

  • Place of publication

    Washington DC

  • Event location

    Washington, D.C., USA

  • Event date

    Dec 12, 2010

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article