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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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Abstract

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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Data Availability

The dataset that was generated for analysis during the current study is available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Amit Krishan Kumar

  2. Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Amit Krishan Kumar

  3. Faculty of Chinese Language, School of Foreign Language, Duy Tan University, Da Nang, Vietnam

    Nguyễn Ngọc Mai

  4. State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Ke Tian & Yuanqing Xia

Authors
  1. Amit Krishan Kumar
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  2. Nguyễn Ngọc Mai
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  3. Ke Tian
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  4. Yuanqing Xia
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Correspondence to Amit Krishan Kumar.

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The authors declare no conflict of interest. The authors declare that this manuscript is original work and that no funds, grants, or other supports were received during the preparation of this manuscript. All authors have contributed equally to the work.

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Kumar, A.K., Mai, N.N., Tian, K. et al. Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks. SN COMPUT. SCI. 4, 807 (2023). https://doi.org/10.1007/s42979-023-02351-6

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