How distance metrics influence missing data imputation with k-nearest neighbours

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Santos, MS (Autor) Outra; Pedro Henriques Abreu (Autor) Outra; Wilk, S (Autor) Outra; Santos, J (Autor) Outra

Revista

Título: Pattern Recognition Letters

Vol. 136

Páginas: 111-119

ISSN: 0167-8655

Editora: Elsevier

Indexação

ISI Web of Knowledge - 30 Citações

Scopus - 35 Citações

Outras Informações

ID Authenticus: P-00S-9GF

DOI: 10.1016/j.patrec.2020.05.032

Abstract (EN): In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated and compared for k-nearest neighbours imputation. In this work, we study the effect of several heterogeneous distances on k-nearest neighbours imputation on a large benchmark of publicly-available datasets.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9