X-Som and L-Som: A Nested Approach for Missing Value Imputation
Abstract
In This Paper, a New Method for the Determination of Missing Values in Temporal Databases is Presented. This One 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 Nested Approach Leads to a Significant Improvement of the Estimation of the Missing Values. an Application of the Determination of Missing Values for Hedge Fund Return Database is Presented.
Recommended Citation
P. Merlin et al., "X-Som and L-Som: A Nested Approach for Missing Value Imputation," ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pp. 83 - 88, European Symposium on Artificial Neural Networks, Dec 2009.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-293030709-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Dec 2009
