An Approach Based on Feature Selection for Missing Value Imputation

Küçük Resim Yok

Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Science and Business Media Deutschland GmbH

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Today, with the spread of technologies such as the internet of things and data acquisition from sensors, the data obtained has increased. The size of the data produced by other sources, especially digital platforms, is increasing day by day. This increase in data production enables the development of effective artificial intelligence applications and in-depth analysis. However, in many data collection processes, missing values are included in the data set due to operational problems or different reasons. This situation is expressed as a data quality problem in the literature. It is possible that the analysis to be made on this data will be negatively affected by this situation. Various statistical techniques and machine learning-based techniques exist in the literature for filling missing values. In this study, an approach is put forward that suggests missing values imputation based on the consistency of the sample with missing values with other samples in the data set. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Açıklama

International Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- -- 264409

Anahtar Kelimeler

Consistency Based, Imputation, Missing values

Kaynak

Lecture Notes in Networks and Systems

WoS Q Değeri

Scopus Q Değeri

Q4

Cilt

307

Sayı

Künye