Self-organizing maps for imputation of missing data in incomplete data matrices
| Autor: | Daniel Cicerone, Jorge Federico Magallanes, Jure Zupan, Laura Folguera |
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| Rok vydání: | 2015 |
| Předmět: |
Self-organizing map
Training set Artificial neural network Process Chemistry and Technology Missing data computer.software_genre Computer Science Applications Analytical Chemistry Missing data imputation Statistics Effective treatment Imputation (statistics) Data mining computer Spectroscopy Software Mathematics |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 143:146-151 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/j.chemolab.2015.03.002 |
| Popis: | The problem of incomplete data matrices is repeatedly found in large databases, posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imputation under the concept of distance object per one weight , to predict physicochemical parameters of water samples in a data set where concentrations of different analytes were missed. The method was evaluated according to two different possibilities: (a) including vectors of samples with and without missing data in the training data set and (b) pre-training a SOM for a data set with no missing values and then making imputations for a second data set (prediction set) of samples with missing values. Evaluations were made using a surface water data set of 270 samples from Reconquista River, in Buenos Aires Province, Argentina, by artificially setting a range of 17% to 39% of the data to missing. Results were compared to imputations made through professional criteria. SOMs gave reasonable estimates, with no statistically significant differences from estimates made through professional criteria, proving thus to be a suitable time-saving imputation method. |
| Databáze: | OpenAIRE |
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