Multi-Objective Genetic Programming for Visual Analytics
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gp-bibliography.bib Revision:1.8081
- @InProceedings{icke:2011:EuroGP,
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author = "Ilknur Icke and Andrew Rosenberg",
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title = "Multi-Objective Genetic Programming for Visual
Analytics",
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booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
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year = "2011",
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month = "27-29 " # apr,
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editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
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series = "LNCS",
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volume = "6621",
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publisher = "Springer Verlag",
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address = "Turin, Italy",
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pages = "322--334",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming: poster",
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DOI = "doi:10.1007/978-3-642-20407-4_28",
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abstract = "Visual analytics is a human-machine collaboration to
data modelling where extraction of the most informative
features plays an important role. Although feature
extraction is a multi-objective task, the traditional
algorithms either only consider one objective or
aggregate the objectives into one scalar criterion to
optimise. In this paper, we propose a Pareto-based
multi-objective approach to feature extraction for
visual analytics applied to data classification
problems. We identify classifiability, visual
interpretability and semantic interpretability as the
three equally important objectives for feature
extraction in classification problems and define
various measures to quantify these objectives. Our
results on a number of benchmark datasets show
consistent improvement compared to three standard
dimensionality reduction techniques. We also argue that
exploration of the multiple Pareto-optimal models
provide more insight about the classification problem
as opposed to a single optimal solution.",
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notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
- }
Genetic Programming entries for
Ilknur Icke
Andrew Rosenberg
Citations