Comparison Between Artificial Neural Net and Pseudo Neural Net Classification in Iris Dataset Case
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F13%3A43870526" target="_blank" >RIV/70883521:28140/13:43870526 - 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
Comparison Between Artificial Neural Net and Pseudo Neural Net Classification in Iris Dataset Case
Original language description
This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier and comparison with classical artificial neural nets. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The proposed method utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.
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/ED2.1.00%2F03.0089" target="_blank" >ED2.1.00/03.0089: The Centre of Security, Information and Advanced Technologies (CEBIA-Tech)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
MENDEL 2013 19th International Conference on Soft Computing
ISBN
978-80-214-4755-4
ISSN
1803-3814
e-ISSN
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Number of pages
6
Pages from-to
239-244
Publisher name
Vysoké učení technické v Brně, Fakulta strojního inženýrství
Place of publication
Brno
Event location
Brno
Event date
Jun 26, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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