Reconstructing Causal Networks From Temporal Data - A Genetic Programming Based Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{Kandpal:thesis,
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author = "Manoj Kandpal",
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title = "Reconstructing Causal Networks From Temporal Data - A
Genetic Programming Based Approach",
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school = "Department of Chemical and Biomolecular Engineering,
National University of Singapore",
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year = "2013",
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address = "Singapore",
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keywords = "genetic algorithms, genetic programming, GPVIM,
Relationships, Causality, Biological Networks, Vector
Autoregressive Modelling, Multivariate Data Analysis",
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URL = "https://core.ac.uk/download/pdf/48808545.pdf",
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URL = "http://scholarbank.nus.edu.sg/handle/10635/118589",
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URL = "http://scholarbank.nus.edu.sg/bitstream/10635/118589/1/KandpalM.pdf",
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size = "251 pages",
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abstract = "This work details a new systematic approach based on
Genetic Programming for finding out relationships and
causality among different variables in a multivariate
system and present them in a network form. The main
focus is on analysing temporal output of biological
phenomenon. The developed GP based Variable Interaction
Methodology (GPVIM) can be used to analyse multivariate
temporal data such that the inherent interactions could
be represented in the form of Multivariate Vector
Autoregressive Model-guided relationship network. The
methodology is further improved by use of quicker
analysis methods such as Correlation, Granger
Causality, and Dynamic Bayesian Network, as mode of
providing pre-cooked data for GPVIM. This helped in
resolving problems associated with large number of
variables and in improving the accuracy of the final
networks. The methodology has been found promising
compare to other available methods, for practical
network reconstruction problems, with higher accuracy
and specificity.",
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notes = "supervisor Lakshminarayanan Samavedham",
- }
Genetic Programming entries for
Manoj Kandpal
Citations