Abstract
Unbalanced datasets generate difficulties in designing good classification models because those classes that are represented by the most numerous training sets are harmfully preferred. For this reason, learning sets are often balanced by adding some synthetic feature vectors or by reducing the most numerous learning sets.
High-dimensional learning sets give possibility to design complex layer of linear classifiers. Such layers can also be used for balancing purposes. In this approach, averaging of a small number of feature vectors is partially complemented by averaging vertices based on balanced feature subsets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Duda, O.R., Hart, P.E., Stork, D.G.: Pattern Classification. J. Wiley, New York (2001)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Bobrowski, L.: complex layers of formal neurons. In: Engineering Applications of Neural Networks - EAAAI/EANN 2022, EANN 2022, pp. 81–89. Springer, Heidelberg (2022)
Bobrowski, L.: Data Exploration and Linear Separability, pp. 1–172. Lambert Academic Publishing, Saarbrücken 2019
Boser, B.E., Guyon, I., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop of Computational Learning Theory, vol. 5, pp. 144–152. ACM, Pittsburgh (1992)
Bobrowski, L.: Computing on vertices in data mining, pp. 1–19. Data mining, Intech Open (2021)
Bobrowski, L., Łukaszuk, T.: Relaxed linear separability (RLS) approach to feature (gene) subset selection. In: Xia, X (ed.) Selected Works in Bioinformatics, INTECH, pp. 103–118 (2011)
Bobrowski, L., Zabielski, P.: Classification model with collinear grouping of features. J. Inf. Telecommun. 7(1), 73–88 (2023)
Acknowledgments
The presented study was supported by the grant WZ/WI-IIT/4/2023 from the Bialystok University of Technology and funded from the resources for research by the Polish Ministry of Science and Higher Education.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bobrowski, L. (2023). Balancing High-Dimensional Datasets with Complex Layers. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-34204-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34203-5
Online ISBN: 978-3-031-34204-2
eBook Packages: Computer ScienceComputer Science (R0)