1. Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database
- Author
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Francesco Corona, Yoan Miche, Bertrand Maillet, Paul Merlin, Eric Séverin, Amaury Lendasse, and Antti Sorjamaa
- Subjects
Database ,Computer science ,business.industry ,Computation ,computer.software_genre ,Machine learning ,Missing data ,Bayesian information criterion ,Missing value imputation ,Imputation (statistics) ,Data mining ,Artificial intelligence ,Linear combination ,business ,computer ,Sparse regression - Abstract
This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validation procedure, thus speeding up the computation significantly. Furthermore, the accuracy of the filling is improved, as demonstrated in the experiments.
- Published
- 2009
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