| dc.creator | Folguera, Laura | |
| dc.creator | Zupan, Jure | |
| dc.creator | Cicerone, Daniel | |
| dc.creator | Magallanes, Jorge | |
| dc.date | 2015-03 | |
| dc.identifier | Folguera, L. et al (2015). Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices. En: Chemometrics and Intelligent Laboratory Systems. Elsevier Science 143, 146-151 | |
| dc.identifier | 0169-7439 | |
| dc.identifier | https://ri.unsam.edu.ar/handle/123456789/1009 | |
| dc.description | 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. | |
| dc.description | Fil: Laura Folguera. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina. | |
| dc.description | Fil: Jure Zupan. National Institute of Chemistry; Ljubljana. Slovenia. | |
| dc.description | Fil: Daniel Cicerone. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina. | |
| dc.description | Fil: Jorge Magallanes. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina. | |
| dc.format | application/pdf | |
| dc.format | pp. 146-151 | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Elsevier Science Bv | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | |
| dc.rights | Creative Commons Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5) | |
| dc.source | Chemometrics and Intelligent Laboratory Systems. 143: 146-151 (2015) Elsevier B.V. | |
| dc.source | http://dx.doi.org/10.1016/j.chemolab.2015.03.002 | |
| dc.subject | CHEMOMETRICS | |
| dc.subject | ARTIFICIAL NEURAL NETWORK | |
| dc.subject | SELF-ORGANIZING MAPS | |
| dc.subject | MISSING DATA IMPUTATION | |
| dc.subject | ENVIRONMENTAL DATA SET | |
| dc.subject | CIENCIAS QUÍMICAS | |
| dc.subject | CIENCIAS EXACTAS Y NATURALES | |
| dc.title | Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:ar-repo/semantics/artículo | |
