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Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). F...

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Autores principales: Zhou, Shang-Ming, Lyons, Ronan A., Brophy, Sinead, Gravenor, Mike B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522708/
https://www.ncbi.nlm.nih.gov/pubmed/23272108
http://dx.doi.org/10.1371/journal.pone.0051468
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author Zhou, Shang-Ming
Lyons, Ronan A.
Brophy, Sinead
Gravenor, Mike B.
author_facet Zhou, Shang-Ming
Lyons, Ronan A.
Brophy, Sinead
Gravenor, Mike B.
author_sort Zhou, Shang-Ming
collection PubMed
description The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.
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spelling pubmed-35227082012-12-27 Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data Zhou, Shang-Ming Lyons, Ronan A. Brophy, Sinead Gravenor, Mike B. PLoS One Research Article The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data. Public Library of Science 2012-12-14 /pmc/articles/PMC3522708/ /pubmed/23272108 http://dx.doi.org/10.1371/journal.pone.0051468 Text en © 2012 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhou, Shang-Ming
Lyons, Ronan A.
Brophy, Sinead
Gravenor, Mike B.
Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data
title Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data
title_full Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data
title_fullStr Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data
title_full_unstemmed Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data
title_short Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data
title_sort constructing compact takagi-sugeno rule systems: identification of complex interactions in epidemiological data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522708/
https://www.ncbi.nlm.nih.gov/pubmed/23272108
http://dx.doi.org/10.1371/journal.pone.0051468
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