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The Issue of Efficient Generation of Generalized Features in Algorithmic Classification Tree Methods

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Data Stream Mining & Processing (DSMP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1158))

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Abstract

The paper studies the basic issue of methods of constructing models of algorithmic classification trees which is the problem of generating generalized features in their structure. There has been suggested a simple and efficient method of approximating the initial training dataset with the help of a set of generalized features of the type of geometric objects in feature space of the current application problem. This approach allows ensuring the necessary accuracy of classification trees, reducing the structural (constructional) complexity and achieving the appropriate performance indicators of the model. Based on the proposed algorithmic scheme of the training set coverage there has been developed the corresponding software which enables the work with a set of different-type applied problems of data processing. Hence, a simple, efficient and economical approximation of the initial training set provides the appropriate speed, level of complexity of the classification scheme, which ensures the simple and complete recognition (coverage) of sets of discrete objects.

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Authors and Affiliations

  1. Uzhhorod National University, Uzhgorod, Ukraine

    Igor Povkhan, Maksym Lupei, Mykhailo Kliap & Vasyl Laver

Authors
  1. Igor Povkhan
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  2. Maksym Lupei
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  3. Mykhailo Kliap
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  4. Vasyl Laver
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Corresponding author

Correspondence to Igor Povkhan .

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Editors and Affiliations

  1. Department of Informatics, Univerzita Jana Evangelisty Purkyně v Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev

  2. GeoGuard, Kharkiv, Ukraine

    Dmytro Peleshko

  3. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Olena Vynokurova

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Povkhan, I., Lupei, M., Kliap, M., Laver, V. (2020). The Issue of Efficient Generation of Generalized Features in Algorithmic Classification Tree Methods. In: Babichev, S., Peleshko, D., Vynokurova, O. (eds) Data Stream Mining & Processing. DSMP 2020. Communications in Computer and Information Science, vol 1158. Springer, Cham. https://doi.org/10.1007/978-3-030-61656-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-61656-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61655-7

  • Online ISBN: 978-3-030-61656-4

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