Imputing missing values from low quality data by NIP tool

An important aspect to consider in applications which work with great volumes of data is that frequently these data are of low quality and also cannot be use other types of data. The field of Soft Computing has dealt, among other things, with developing techniques that will be able to work with thes...

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Bibliographic Details
Published in2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp. 1 - 8
Main Authors Martinez, Raquel, Cadenas, Jose M., Garrido, M. Carmen, Martinez, Alejandro
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2013
Subjects
Covariance matrices
Data mining
Data models
Imputation of missing values
Low quality data
Noise
Predictive models
Robustness
Soft Computing
software tool for Soft Computing
Uncertainty
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Summary:An important aspect to consider in applications which work with great volumes of data is that frequently these data are of low quality and also cannot be use other types of data. The field of Soft Computing has dealt, among other things, with developing techniques that will be able to work with these types of low quality data in a suitable way, respecting the true origin of these data. In this paper we present a method to carry out the imputation of missing values from information that may be of low quality when another possibility is not available. The method is based on a predictable model. The imputation method developed is incorporated into the software tool NIP increasing its functionality of imputation/replacement of low quality values.
ISSN:1098-7584
DOI:10.1109/FUZZ-IEEE.2013.6622389