Handling Missing Values via a Neural Selective Input Model

Title:

Handling Missing Values via a Neural Selective Input Model

Creator:

Lopes, Noel and Riberio, Bernadete

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:52b5f55d-73e0-4d09-98db-d318cb5bf68e
uuid:52b5f55d-73e0-4d09-98db-d318cb5bf68e
doi:10.14311/NNW.2012.22.021

Subject:

Missing Values, Neural Networks, Back-Propagation, and Multiple Back-Propagation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Missing data represent an ubiquitous problem with numerous and diverse causes. Handling Missing Value properly is a crucial issue, in particular in Machine Learning and pattern recognition. To date, the only option available for standard Neural Network to handle this problem has been to rely on pre-processing techniques such as imputation for estimating the missing data values, which limited considerably the scope of their application. To circumvent this limitation we propose a Neural Selective Input Model that accommodates different transparent and bound models, while providing support for Neural Network to handle Missing Value directly. By embedding the mechanisms to support Missing Value we can obtain better models that reflect the uncertainty caused by unknown values. Experiments on several UCI datasets with both different distributions and proportion of Missing Value show that the Neural Selective Input Model approach is very robust and yields good to excellent results. Furthermore, the Neural Selective Input Model performs better than the state-of-the-art imputation techniques either with higher prevalence of Missing Value in a large number of features or with a significant proportion of Missing Value, while delivering competitive performance in the remaining cases. We demonstrate the usefulness and validity of the Neural Selective Input Model, making this a first-class method for dealing with this problem.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:4

Harvested from:

CDK

Metadata only:

false