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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Pseudo Neural Networks For Iris Data Classification |
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Authors: |
Zuzana Kominkova Oplatkova,
Roman Senkerik, Ales Kominek |
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Published in:
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(2014).ECMS 2014 Proceedings edited
by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for
Modeling and Simulation. doi:10.7148/2014 ISBN:
978-0-9564944-8-1 28th
European Conference on Modelling and Simulation, Brescia,
Italy, May 27th – 30th,
2014 |
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Citation
format: |
Zuzana Kominkova Oplatkova, Roman Senkerik, Ales Kominek (2014). Pseudo Neural Networks For Iris Data
Classification, ECMS 2014 Proceedings edited by: Flaminio
Squazzoni, Fabio Baronio,
Claudia Archetti, Marco Castellani European
Council for Modeling and Simulation. doi:10.7148/2014-0387 |
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DOI: |
http://dx.doi.org/10.7148/2014-0387 |
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Abstract: |
This research deals with a
novel approach to classification. Iris data was used for the experiments. Classical
artificial neural networks, where a relation between inputs and outputs is
based on the mathematical transfer functions and optimized numerical weights,
was an inspiration for this work. Artificial neural networks need to optimize
weights, but the structure and transfer functions are usually set up before
the training. The proposed method utilizes the symbolic regression for
synthesis of a whole structure, i.e. the relation between inputs and
output(s). This paper differs from the previous approach where only one output
pseudo node was used even for more classes. In this case, there were
synthesized more node output equations as in classical artificial neural
networks. The benchmark was iris data as in previous research. For experimentation,
Differential Evolution (DE) for the main procedure and also for
meta-evolution version of analytic programming (AP) was used. |
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