Discovery in Hydrating Plaster Using Machine Learning Methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8178
- @InProceedings{conf/dis/DevaneyH02,
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author = "Judith Ellen Devaney and John G. Hagedorn",
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title = "Discovery in Hydrating Plaster Using Machine Learning
Methods",
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booktitle = "5th International Conference on Discovery Science, DS
2002",
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year = "2002",
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editor = "Steffen Lange and Ken Satoh and Carl H. Smith",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "2534",
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pages = "47--58",
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address = "L{\"u}beck, Germany",
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month = nov # " 24-26",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "3-540-00188-3",
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URL = "http://math.nist.gov/mcsd/savg/papers/discov2002.pdf",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.2341",
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DOI = "doi:10.1007/3-540-36182-0_7",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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contributor = "CiteSeerX",
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language = "en",
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oai = "oai:CiteSeerXPSU:10.1.1.138.2341",
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abstract = "We apply multiple machine learning methods to obtain
concise rules that are highly predictive of
scientifically meaningful classes in hydrating plaster
over multiple time periods. We use three dimensional
data obtained through X-ray microtomography at greater
than one micron resolution per voxel at five times in
the hydration process: powder, after 4 hours, 7 hours,
15.5 hours, and after 6 days of hydration. Using
statistics based on locality, we create vectors
containing eight attributes for subsets of size 1000 of
the data and use the autoclass unsupervised
classification system to label the attribute vectors
into three separate classes. Following this, we use the
C5 decision tree software to separate the three classes
into two parts: class 0 and 1, and class 0 and 2. We
use our locally developed procedural genetic
programming system, GPP, to create simple rules for
these. The resulting collection of simple rules are
tested on a separate 1000 subset of the plaster
datasets that had been labeled with their autoclass
predictions. The rules were found to have both high
sensitivity and high positive predictive value. The
classes accurately identify important structural
components in the hydrating plaster. Moreover, the
rules identify the center of the local distribution as
a critical factor in separating the classes.",
- }
Genetic Programming entries for
Judith E Devaney
John G Hagedorn
Citations