Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
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Publications

Citation

BibTex format

@article{Herrera-Vega:2018:10.1016/j.engappai.2018.01.001,
author = {Herrera-Vega, J and Orihuela-Espina, F and Ibarguengoytia, PH and Garcia, UA and Rosado, D-EV and Morales, EF and Enrique, Sucar L},
doi = {10.1016/j.engappai.2018.01.001},
journal = {Engineering Applications of Artificial Intelligence},
pages = {1--15},
title = {A local multiscale probabilistic graphical model for data validation and reconstruction, and its application in industry},
url = {http://dx.doi.org/10.1016/j.engappai.2018.01.001},
volume = {70},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The detection and subsequent reconstruction of incongruent data in time series by means of observation of statistically related information is a recurrent issue in data validation. Unlike outliers, incongruent observations are not necessarily confined to the extremes of the data distribution. Instead, these rogue observations are unlikely values in the light of statistically related information. This paper proposes a multiresolution Bayesian network model for the detection of rogue values and posterior reconstruction of the erroneous sample for non-stationary time-series. Our method builds local Bayesian Network models that best fit to segments of data in order to achieve a finer discretization and hence improve data reconstruction. Our local multiscale approach is compared against its single-scale global predecessor (assumed as our gold standard) in the predictive power and of this, both error detection capabilities and error reconstruction capabilities are assessed. This parameterization and verification of the model are evaluated over three synthetic data source topologies. The virtues of the algorithm are then further tested in real data from the steel industry where the aforementioned problem characteristics are met but for which the ground truth is unknown. The proposed local multiscale approach was found to dealt better with increasing complexities in data topologies.
AU  - Herrera-Vega,J
AU  - Orihuela-Espina,F
AU  - Ibarguengoytia,PH
AU  - Garcia,UA
AU  - Rosado,D-EV
AU  - Morales,EF
AU  - Enrique,Sucar L
DO  - 10.1016/j.engappai.2018.01.001
EP  - 15
PY  - 2018///
SN  - 0952-1976
SP  - 1
TI  - A local multiscale probabilistic graphical model for data validation and reconstruction, and its application in industry
T2  - Engineering Applications of Artificial Intelligence
UR  - http://dx.doi.org/10.1016/j.engappai.2018.01.001
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000428484900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/70140
VL  - 70
ER  -
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