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Distance Metrics in Clustering and Weighted Scoring Algorithm

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Progress in Image Processing, Pattern Recognition and Communication Systems (CORES 2021, IP&C 2021, ACS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 255))

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Abstract

One of the current challenges for the supervised classification methods is to obtain acceptable values of the performance measures for an imbalanced dataset. There is a significant disproportion in the number of objects from different class labels in datasets with a high imbalanced ratio. This article analyzes the clustering and weighted scoring algorithm based on estimating the number of clusters that consider the minimum number of objects from the minority class label in each cluster. This algorithm uses the distance metric when determining the value of the score function. Therefore, this article aims to analyze the impact of selecting the distance metric on the six classification performance measures’ value. The performed experiments show that the Euclidean distance allows obtaining the best classification results for imbalanced datasets.

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Notes

  1. 1.

    Repository link: https://github.com/w4k2/cws-enc-cores21.

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Acknowledgements

This work was supported by the Polish National Science Centre under the grant No. 2017/25/B/ST6/01750 as well as by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland

    Jakub Klikowski & Robert Burduk

Authors
  1. Jakub Klikowski
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  2. Robert Burduk
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Corresponding author

Correspondence to Robert Burduk .

Editor information

Editors and Affiliations

  1. Institute of Computer Science and Telecommunications, University of Science and Technlogy, Bydgoszcz, Poland

    Michal ChoraĹ›

  2. Institute of Computer Science and Telecommunications, University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. ChoraĹ›

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzyński

  4. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Paweł Trajdos

  5. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Jerzy PejaĹ›

  6. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Tomasz Hyla

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Klikowski, J., Burduk, R. (2022). Distance Metrics in Clustering and Weighted Scoring Algorithm. In: Choraś, M., Choraś, R.S., Kurzyński, M., Trajdos, P., Pejaś, J., Hyla, T. (eds) Progress in Image Processing, Pattern Recognition and Communication Systems. CORES IP&C ACS 2021 2021 2021. Lecture Notes in Networks and Systems, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-81523-3_3

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