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The Data Dimensionality Reduction and Features Weighting in the Classification Process Using Forest Optimization Algorithm

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Intelligent Information and Database Systems: Recent Developments (ACIIDS 2019)

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Abstract

The paper presents the data dimensionality reduction in the classification process, with a special presentation of using the ability of features weighting by determining the level of importance of a given attribute in the data vector. This reduction was implemented using the Forest Optimization Algorithm (FOA) and the use of a classifier allowing to enter the importance of each attribute for a data vector. The paper presents both, a description of the capability of using the FOA algorithm as well as the possibility of introducing modifications which allows to regulate the objective function between the obtained classification result and the number of reduced features. The conducted tests and obtained results were also presented. At the end of paper, a summary and the final conclusions are provided.

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Acknowledgements

This work was supported by BKM-509/RAU2/2017 (DK) and BK-213/RAu2/2018 (RB) grants from the Institute of Informatics, Silesian University of Technology, Gliwice, Poland.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Daniel Kostrzewa & Robert Brzeski

Authors
  1. Daniel Kostrzewa
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  2. Robert Brzeski
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Corresponding author

Correspondence to Daniel Kostrzewa .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Maciej Huk

  2. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

  3. Department of Management, Gdańsk University of Technology, Gdańsk, Poland

    Edward Szczerbicki

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Kostrzewa, D., Brzeski, R. (2020). The Data Dimensionality Reduction and Features Weighting in the Classification Process Using Forest Optimization Algorithm. In: Huk, M., Maleszka, M., Szczerbicki, E. (eds) Intelligent Information and Database Systems: Recent Developments. ACIIDS 2019. Studies in Computational Intelligence, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-030-14132-5_8

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