Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
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 physicoche...
| Autori principali: | , , , |
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| Natura: | Texto completo |
| Lingua: | Inglés |
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Elsevier Science Bv
2015
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| Soggetti: | |
| Accesso online: | https://ri.unsam.edu.ar/handle/123456789/1009 |
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ds-123456789-1009 |
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| record_format |
dspace |
| institution |
Repositorio Institucional |
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RI |
| language |
Inglés |
| topic |
CHEMOMETRICS ARTIFICIAL NEURAL NETWORK SELF-ORGANIZING MAPS MISSING DATA IMPUTATION ENVIRONMENTAL DATA SET CIENCIAS QUÍMICAS CIENCIAS EXACTAS Y NATURALES |
| spellingShingle |
CHEMOMETRICS ARTIFICIAL NEURAL NETWORK SELF-ORGANIZING MAPS MISSING DATA IMPUTATION ENVIRONMENTAL DATA SET CIENCIAS QUÍMICAS CIENCIAS EXACTAS Y NATURALES Folguera, Laura Zupan, Jure Cicerone, Daniel Magallanes, Jorge Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices |
| 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. |
| format |
Texto completo |
| author |
Folguera, Laura Zupan, Jure Cicerone, Daniel Magallanes, Jorge |
| author_facet |
Folguera, Laura Zupan, Jure Cicerone, Daniel Magallanes, Jorge |
| author_sort |
Folguera, Laura |
| title |
Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices |
| title_short |
Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices |
| title_full |
Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices |
| title_fullStr |
Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices |
| title_full_unstemmed |
Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices |
| title_sort |
self-organizing maps for imputation of missing data in incomplete data matrices |
| publisher |
Elsevier Science Bv |
| publishDate |
2015 |
| url |
https://ri.unsam.edu.ar/handle/123456789/1009 |
| work_keys_str_mv |
AT folgueralaura selforganizingmapsforimputationofmissingdatainincompletedatamatrices AT zupanjure selforganizingmapsforimputationofmissingdatainincompletedatamatrices AT ciceronedaniel selforganizingmapsforimputationofmissingdatainincompletedatamatrices AT magallanesjorge selforganizingmapsforimputationofmissingdatainincompletedatamatrices |
| _version_ |
1747968924248440832 |