Abstract
In healthcare domains, dealing with missing data is crucial since absent observations compromise the reliability of decision support models. K-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patients to replace missing values. Nevertheless, its performance largely depends on the distance function used to evaluate such similarity. In the literature, k-nearest neighbours imputation frequently neglects the nature of data or performs feature transformation, whereas in this work, we study the impact of different heterogeneous distance functions on k-nearest neighbour imputation for biomedical datasets. Our results show that distance functions considerably impact the performance of classifiers learned from the imputed data, especially when data is complex.
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Acknowledgements
This work was supported in part by the project NORTE-01-0145-FEDER-000027 (Norte Portugal Regional Operational Programme – Norte 2020) and in part by the FCT Research Grant SFRH/BD/138749/2018.
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Santos, M.S., Abreu, P.H., Wilk, S., Santos, J. (2020). Assessing the Impact of Distance Functions on K-Nearest Neighbours Imputation of Biomedical Datasets. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_43
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DOI: https://doi.org/10.1007/978-3-030-59137-3_43
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