X-Som and L-Som: A Double Classification Approach for Missing Value Imputation

Author

Paul Merlin
Antti Sorjamaa
Bertrand Maillet
Amaury Lendasse, Missouri University of Science and TechnologyFollow

Abstract

In This Paper, a New Method for the Determination of Missing Values in Temporal Databases is Presented. It is based on a Robust Version of a Nonlinear Classification Algorithm Called Self-Organizing Maps and It Consists of a Combination of Two Classifications in Order to Take Advantage of Spatial as Well as Temporal Dependencies of the Dataset. This Double Classification Leads to a Significant Improvement of the Estimation of the Missing Values. an Application of the Missing Value Imputation for Hedge Fund Returns is Presented. © 2010 Elsevier B.v.

Recommended Citation

P. Merlin et al., "X-Som and L-Som: A Double Classification Approach for Missing Value Imputation," Neurocomputing, vol. 73, no. 7 thru 9, pp. 1103 - 1108, Elsevier, Mar 2010.

The definitive version is available at https://doi.org/10.1016/j.neucom.2009.11.019

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Empirical orthogonal function; Missing value completion; Self-organizing maps

International Standard Serial Number (ISSN)

0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Mar 2010