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Visualization techniques utilizing the sensitivity analysis of models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F07%3A03136553" target="_blank" >RIV/68407700:21230/07:03136553 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Visualization techniques utilizing the sensitivity analysis of models

  • Original language description

    Models of real world systems are being increasingly generated from data that describes the behaviour of systems. Data mining techniques, such as Artificial Neural Networks (ANN), generate models almost independently and deliver accurate models in a veryshort time. These models have complex internal structures that are difficult to interpret and we have very limited information about the credibility of their output.In this paper, we present visualization techniques based on sensitivity analysis aimed todiscover relationships between variables and to recognize regions where the output is credible and where it is not. We have developed visualization techniques both for regression and classification problems. Finally, we present an algorithm that is ableto automatically locate the most interesting visualizations in the vast multidimensional space of input variables.

  • Czech name

    Visualizační techniky využívající citlivostní analýzu modelů

  • Czech description

    Je problém rozpoznat, kde je odpověď modelu založená na trénovacích datech a kde je čistě náhodná. V tomto článku ukážeme, že pomocí kombinování modelů jsme schopni bezpečně odlišit správné odpovědi modelu jak pro regresní, tak pro klasifikační problémy.Dále představíme algoritmus, který hledá zajímavé vizualizace pomocí šlechtění jejich parametrů.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/KJB201210701" target="_blank" >KJB201210701: Automated Knowledge Extraction</a><br>

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2007

  • 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 2007 Winter Simulation Conference

  • ISBN

    1-4244-1306-0

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    730-737

  • Publisher name

    ACM

  • Place of publication

    New York

  • Event location

    Washington

  • Event date

    Dec 9, 2007

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article