Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F15%3A86096347" target="_blank" >RIV/61989100:27510/15:86096347 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s11227-015-1387-y" target="_blank" >http://dx.doi.org/10.1007/s11227-015-1387-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11227-015-1387-y" target="_blank" >10.1007/s11227-015-1387-y</a>

Result language

angličtina

Original language name

Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach

Original language description

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AE - Management, administration and clerical work

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2015

Confidentiality

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

Name of the periodical

Journal of Supercomputing

ISSN

0920-8542

e-ISSN

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Volume of the periodical

71

Issue of the periodical within the volume

7

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

15

Pages from-to

2397-2411

UT code for WoS article

000357345600004

EID of the result in the Scopus database

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