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Classifiers Accuracy Improvement Based on Missing Data Imputation Cover

Classifiers Accuracy Improvement Based on Missing Data Imputation

Journal of Artificial Intelligence and Soft Computing Research
Volume 8 (2018): Issue 1 (January 2018)
By: Ivan Jordanov,  Nedyalko Petrov and  Alessio Petrozziello  
Open Access
|Nov 2017

Authors

Ivan Jordanov

ivan.jordanov@port.ac.uk

School of Computing, University of Portsmouth, Portsmouth

Nedyalko Petrov

nedyalko.petrov@port.ac.uk

School of Computing, University of Portsmouth, Portsmouth

Alessio Petrozziello

alessio.petrozziello@port.ac.uk

School of Computing, University of Portsmouth, Portsmouth
DOI: https://doi.org/10.1515/jaiscr-2018-0002
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Language: English
Page range: 31 - 48
Submitted on: Feb 14, 2017
Accepted on: Mar 28, 2017
Published on: Nov 1, 2017
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
machine learning,
missing data,
model-based imputation,
neural networks,
random forests,
support vector machines,
radar signal classification
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2017 Ivan Jordanov, Nedyalko Petrov, Alessio Petrozziello, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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