Abstract
The KNN algorithm is affected by overlapping classification, unbalanced data, and K-value selection. It is difficult to apply to some environments with uncertain phenomena. At the same time, the three-way decision is a decision theory that conforms to the human cognitive model with subjective characteristics, so the idea of a three-way decision is introduced into the KNN ensemble learning algorithm and the KNN ensemble learning algorithm based on the three-way decision is proposed. Based on the KNN ensemble learning algorithm, the conditional Probability of each class is calculated and combined with the cost function, which is used to determine the positive domain, negative domain, and boundary domain in the three-way decision theory. This paper performs the three-way decision KNN ensemble learning classification on seven real UCI datasets. The experimental results show that it can effectively improve the classification accuracy and F1-score of the data.
2021 Applied Research Project of Yuncheng University: Specialty Packaging Design and Evaluation based on Three-way Decision and TOPSIS Model (Project No.: CY-2021022).
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References
Yao, Y.: An outline of a theory of three-way decisions. In: Yao, J.T., et al. (eds.) Rough Sets and Current Trends in Computing. LNCS (LNAI), vol. 7413, pp. 1–17. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32115-3_1
Yao, Y.: Rough sets and three-way decisions. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.Z. (eds.) Rough Sets and Knowledge Technology. LNCS (LNAI), vol. 9436, pp. 62–73. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25754-9_6
Yao, Y.Y.: Three-way decision and granular computing. Int. J. Approx. Reason. 103, 107–123 (2018)
Guo, H., et al.: BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification. Eng. App. Artif. Intell. 49,176–193 (2015)
Yu, Y., et al.: An Improved KNN algorithm based on variable precision rough sets. Pattern Recogn. Artif. Intell. 25(4),617–623 (2012)
Chen, L.F., Guo, G.D.: Multi-representatives learning algorithm for nearest neighbour classification. Pattern Recogn. Artif. Intell. 24(6), 882–888 (2011)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern Classification. In: IEEE Trans. Inf. Theory. 13(1), 21–27 (1967)
Li, H.: Statistical Learning Methods. Tsinghua, Beijing (2012)
Pei, Y.P., Shang, A., Liu, M.H., Chen, F.: KNN incremental algorithm based on the three-way decisions. Control Eng. China 27(04), 656–661 (2020)
Zhang, S.C.: Cost-sensitive KNN classification. Neurocomputing. 391p. (prepublish) (2020)
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Jia, X., Li, Y., Wang, P. (2022). KNN Ensemble Learning Integration Algorithm Based on Three-Way Decision. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_26
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DOI: https://doi.org/10.1007/978-3-031-21244-4_26
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