IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i11p422-d1520775.html
   My bibliography  Save this article

SBNNR: Small-Size Bat-Optimized KNN Regression

Author

Listed:
  • Rasool Seyghaly

    (Advanced Network Architectures Laboratory (CRAAX), Universitat Politècnica de Catalunya (UPC) BarcelonaTECH, 08800 Vilanova, Spain)

  • Jordi Garcia

    (Advanced Network Architectures Laboratory (CRAAX), Universitat Politècnica de Catalunya (UPC) BarcelonaTECH, 08800 Vilanova, Spain)

  • Xavi Masip-Bruin

    (Advanced Network Architectures Laboratory (CRAAX), Universitat Politècnica de Catalunya (UPC) BarcelonaTECH, 08800 Vilanova, Spain)

  • Jovana Kuljanin

    (Aeronautical Division, Universitat Politècnica de Catalunya BarcelonaTECH, 08034 Barcelona, Spain)

Abstract

Small datasets are frequent in some scientific fields. Such datasets are usually created due to the difficulty or cost of producing laboratory and experimental data. On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. For this reason, in some cases, low-performance, overfitting models are developed for small-scale data. As a result, it appears necessary to develop methods for dealing with this type of data. In this research, we provide a new and innovative framework for regression problems with a small sample size. The base of our proposed method is the K-nearest neighbors (KNN) algorithm. For feature selection, instance selection, and hyperparameter tuning, we use the bat optimization algorithm (BA). Generative Adversarial Networks (GANs) are employed to generate synthetic data, effectively addressing the challenges associated with data sparsity. Concurrently, Deep Neural Networks (DNNs), as a deep learning approach, are utilized for feature extraction from both synthetic and real datasets. This hybrid framework integrates KNN, DNN, and GAN as foundational components and is optimized in multiple aspects (features, instances, and hyperparameters) using BA. The outcomes exhibit an enhancement of up to 5% in the coefficient of determination ( R 2 score) using the proposed method compared to the standard KNN method optimized through grid search.

Suggested Citation

  • Rasool Seyghaly & Jordi Garcia & Xavi Masip-Bruin & Jovana Kuljanin, 2024. "SBNNR: Small-Size Bat-Optimized KNN Regression," Future Internet, MDPI, vol. 16(11), pages 1-20, November.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:422-:d:1520775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/11/422/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/11/422/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:422-:d:1520775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.