Abstract
Most data analysis techniques, e.g. those faced in [15,16,17,18] usually require datasets without missing data, however, in the current age of large datasets it is becoming more and more common to deal with huge datasets for which some values are missing. For this reason, several techniques for the imputation of missing values have been developed. In the present work, we present one such technique based on the use of multivariate medians within the context of nearest neighbour imputation. Several experiments are carried out proving a better performance than some state-of-the-art methods in presence of noise.
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Acknowledgments
Raúl Pérez-Fernández acknowledges the support of Campus de Excelencia Internacional de la Universidad de Oviedo in collaboration with Banco de Santander.
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Gissi, F., Benedetto, V., Bhandari, P., Pérez-Fernández, R. (2023). On the Use of Multivariate Medians for Nearest Neighbour Imputation. In: Troiano, L., Vaccaro, A., Kesswani, N., Díaz Rodriguez, I., Brigui, I., Pastor-Escuredo, D. (eds) Key Digital Trends in Artificial Intelligence and Robotics. ICDLAIR 2022. Lecture Notes in Networks and Systems, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-30396-8_13
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