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MIMO Pseudo Neural Networks for Iris Data Classification

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Modern Trends and Techniques in Computer Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 285))

Abstract

This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier. Compared to previous research, this paper synthesizes multi-input–multi-output (MIMO) classifiers. Classical artificial neural networks (ANN) were an inspiration for this work. The proposed technique creates a relation between inputs and outputs as a whole structure together with numerical values which could be observed as weights in ANN. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. 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.

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Acknowledgments

This work was supported by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089.

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Correspondence to Zuzana Kominkova Oplatkova .

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Oplatkova, Z.K., Senkerik, R. (2014). MIMO Pseudo Neural Networks for Iris Data Classification. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-319-06740-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-06740-7_15

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