Please use this identifier to cite or link to this item:
|Type:||Artigo de periódico|
|Title:||Predicting missing values with biclustering: A coherence-based approach|
|Author:||de Franca, FO|
Von Zuben, FJ
|Abstract:||In this work, a novel biclustering-based approach to data imputation is proposed. This approach is based on the Mean Squared Residue metric, used to evaluate the degree of coherence among objects of a dataset, and presents an algebraic development that allows the modeling of the predictor as a quadratic programming problem. The proposed methodology is positioned in the field of missing data, its theoretical aspects are discussed and artificial and real-case scenarios are simulated to evaluate the performance of the technique. Additionally, relevant properties introduced by the biclustering process are also explored in post-imputation analysis, to highlight other advantages of the proposed methodology, more specifically confidence estimation and interpretability of the imputation process. (C) 2012 Elsevier Ltd. All rights reserved.|
Missing data imputation
|Editor:||Elsevier Sci Ltd|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.