Missing value imputation on missing completely at random data using multilayer perceptrons

Silva-Ramírez, Esther-Lydia; Pino‐Mejías, Rafael; López-Coello, Manuel; Cubiles-de-la-Vega, María-Dolores

doi:10.1016/j.neunet.2010.09.008

Cited by 137 publications

(44 citation statements)

References 9 publications

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“…In [21] fifteen real and simulated datasets are exposed to a perturbation experiment, based on the random generation of missing values. Several architectures and learning algorithms for the MLP are tested and compared with three classic imputation procedures: mean/mode imputation, regression, and hot-deck [22].…”

Section: Missing Values and Relatedmentioning

confidence: 99%

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

et al. 2018

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Ozone is one of the pollutants with most negative effects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artificial Neural Network models are applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y León (Spain) are gathered in different ways and then analyzed. The results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response.

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Section: Missing Values and Relatedmentioning

confidence: 99%

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

et al. 2018

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“…The group of imputation methods includes a large number of algorithms for estimation of missing values: from simple approaches based on substitution of certain values [11] to the use of model methods [12,13] that approximate dependence of missing values on the obtained data. The most promising are methods of imputation on the basis of data mining algorithms, which are able to discover internal patterns in data and use them for the further process of imputation [14,15].…”

Section: Analysis Of Scientific Literature and The Problem Statementmentioning

confidence: 99%

Development of models for imputation of data from social networks on the basis of an extended matrix of attributes

Slabchenko

Sydorenko

Siebert

2016

EEJET

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“…Among the common observational methods are the box-plots, time series, histogram, ranked data plots and normal probability plots [18,19]. These methods basically detect an outlier value by quantifying how far it lies from the other values.…”

Section: Outliers (Extreme Values)mentioning

confidence: 99%