KNN-Based Algorithm of Hard Case Detection in Datasets for Classification

Kussul, Anton

doi:10.25673/101926

Proceedings of International Conference on Applied Innovation in IT · 2023/03/09 · Vol. 11 · Issue 1 · pp. 113–118

KNN-Based Algorithm of Hard Case Detection in Datasets for Classification

Anton Okhrimenko and Nataliia Kussul

📄 Download PDF DOI: 10.25673/101926

Abstract

The machine learning models for classification are designed to find the best way to separate two or more classes. In case of class overlapping, there is no possible way to clearly separate such data. Any ML algorithm will fail to correctly classify a certain set of datapoints, which are surrounded by a significant number of another class data points at the feature space. However, being able to find such hardcases in a dataset allows using another set of rules than for normal data samples. In this work, we introduce a KNN-based detection algorithm of data points and subspaces for which the classification decision is ambiguous. The algorithm described in details along with demonstration on artificially generated dataset. Also, the possible usecases are discussed, including dataset quality assessment, custom ensemble strategy and data sampling modifications. The proposed algorithm can be used during full cycle of machine learning model developing, from forming train dataset to real case model inference.

Keywords

KNN Dataset Quality Assessment Imbalanced Datasets Hard Cases.

References

H. Abdi and L. J. Williams, “Principal component analysis. Wiley interdisciplinary reviews: computational statistics,” Wiley Interdisplinary Reviews: Computational Statistics, 2010.
L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579-2605, 2008. [Online]. Available: http://jmlr.org/papers/v9/vandermaaten08a.html
W. A. Almutairi and R. Janicki, “On relationships between imbalance and overlapping of datasets,” EPiC Series in Computing, vol. 69, 2020.
V. Garc´ıa, R. A. Mollineda, and J. S. S´anchez, “On the k-NN performance in a challenging scenario of imbalance and overlapping,” Pattern Analysis and Applications, vol. 11, 2008.
M. M. Nwe and K. T. Lynn, “KNN-based overlapping samples filter approach for classification of imbalanced data,” Studies in Computational Intelligence, vol. 845, 2020.
L. Chen, B. Fang, Z. Shang, and Y. Tang, “Tackling class overlap and imbalance problems in software defect prediction,” Software Quality Journal, vol. 26, no. 1, pp. 97–125, Mar 2018. [Online]. Available: https://doi.org/10.1007/s11219-016-9342-6.
Y. Tang and J. Gao, “Improved classification for problem involving overlapping patterns,” IEICE TRANSACTIONS on Information and Systems”, vol. 90, no. 11, pp. 1787–1795, Nov 2007. [Online]. Available: https://doi.org/10.1093/ietisy/e90-d.11.1787.
N. L¨assig, S. Oppold, and M. Herschel, “Metrics and algorithms for locally fair and accurate classifications using ensembles,” Datenbank-Spektrum, vol. 22, 2022.
H. Kaur, H. S. Pannu, and A. K. Malhi, “A systematic review on imbalanced data challenges in machine learning: Applications and solutions,” ACM Comput. Surv., vol. 52, no. 4, aug 2019. [Online]. Available: https://doi.org/10.1145/3343440.
N. Kussul, A. Shelestov, M. Lavreniuk, I. Butko, and S. Skakun, “Deep learning approach for large scale land cover mapping based on remote sensing data fusion,” International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, 2016.