Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/81214
Type: Artigo de periódico
Title: Predicting missing values with biclustering: A coherence-based approach
Author: de Franca, FO
Coelho, GP
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.
Subject: Biclustering
Missing data imputation
Knowledge discovery
Quadratic programming
Country: Inglaterra
Editor: Elsevier Sci Ltd
Rights: fechado
Identifier DOI: 10.1016/j.patcog.2012.10.022
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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