Assessing the Impact of Distance Functions on K-Nearest Neighbours Imputation of Biomedical Datasets
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] pracownik
2020
rozdział w monografii naukowej / referat
angielski
- missing data
- heterogeneous data
- data imputation
- distance functions
- k-nearest neighbours
- biomedical data
EN 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.
26.09.2020
486 - 496
20
70