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Instance Selection Techniques for Large Volumes of Data

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Intelligent Data Engineering and Automated Learning – IDEAL 2023 (IDEAL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14404))

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Abstract

Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that involves eliminating irrelevant and superfluous data while maintaining a high level of classification accuracy. IS, as a strategic filtering mechanism, addresses these challenges by retaining essential instances and discarding hindering elements. This refinement process optimizes classification algorithms, enabling them to excel in handling extensive datasets. In this research, IS offers a promising avenue to strengthen the effectiveness of classification in various real-world applications.

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Authors and Affiliations

  1. Department of Electronic, Computer Systems and Automation Engineering, International University of Andalusia, Huelva, Spain

    Marco Antonio Peña Cubillos & Antonio Javier Tallón Ballesteros

Authors
  1. Marco Antonio Peña Cubillos
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  2. Antonio Javier Tallón Ballesteros
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Corresponding authors

Correspondence to Marco Antonio Peña Cubillos or Antonio Javier Tallón Ballesteros .

Editor information

Editors and Affiliations

  1. University of Évora, Évora, Portugal

    Paulo Quaresma

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Manchester, Manchester, UK

    Hujun Yin

  4. University of Évora, Évora, Portugal

    Teresa Gonçalves

  5. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julian

  6. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cubillos, M.A.P., Ballesteros, A.J.T. (2023). Instance Selection Techniques for Large Volumes of Data. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_49

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  • DOI: https://doi.org/10.1007/978-3-031-48232-8_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48231-1

  • Online ISBN: 978-3-031-48232-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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