Experiments on data classification using relative Entropy

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099080" target="_blank" >RIV/61989100:27240/16:86099080 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-26227-7_22" target="_blank" >http://dx.doi.org/10.1007/978-3-319-26227-7_22</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-26227-7_22" target="_blank" >10.1007/978-3-319-26227-7_22</a>

Result language
angličtina
Original language name
Experiments on data classification using relative Entropy
Original language description
Data classification is one of the basic tasks in data mining. In this paper, we propose a new classifier based on relative entropy, where data to particular class assignment is made by the majority good guess criteria. The presented approach is intended to be used when relations between datasets and assignment classes are rather complex, nonlinear, or with logical inconsistencies; because such datasets can be too complex to be classified by ordinary methods of decision trees or by the tools of logical analysis. The relative entropy evaluation of associative rules can be simple to interpret and offers better comprehensibility in comparison to decision trees and artificial neural networks. (C) Springer International Publishing Switzerland 2016.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Advances in Intelligent Systems and Computing. Volume 403
ISBN
978-3-319-26225-3
ISSN
2194-5357
e-ISSN
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Number of pages
10
Pages from-to
233-242
Publisher name
Springer Verlag
Place of publication
London
Event location
Wrocław
Event date
May 25, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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