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Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis

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Mendel 2015 (ICSC-MENDEL 2016)

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This research deals with a novel approach to classification. New classifiers are synthesized as a complex structure via evolutionary symbolic computation techniques. Compared to previous research, this paper synthesizes multi-input-multi-output (MIMO) classifiers with different cost function based on distance measurements. An inspiration for this work came from the field of artificial neural networks (ANN). 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. Distances used in cost functions were: Manhattan (absolute distances of output vectors), Euclidean, Chebyshev (maximum distance value), Canberra distance, Bray – Curtis. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used Iris data (a known benchmark for classifiers) was used for testing of the proposed method.

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This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also 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. (2015). Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham.

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  • Print ISBN: 978-3-319-19823-1

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