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Discovery in Hydrating Plaster Using Machine Learning Methods

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Book cover Discovery Science (DS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2534))

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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 1003 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 1003 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 comonents in the hydrating plaster. Morover, the rules identify the center of the local distribution as a critical factor in separating the classes.

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Authors and Affiliations

  1. National Institute of Standards and Technology, 20899-8951, Gaithersburg, MD, USA

    Judith E. Devaney & John G. Hagedorn

Authors
  1. Judith E. Devaney
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  2. John G. Hagedorn
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Editor information

Editors and Affiliations

  1. Deutsches Forschungszentrum für Künstliche Intelligenz, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Steffen Lange

  2. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Ken Satoh

  3. Department of Computer Science, University of Maryland, College Park, 20742, Maryland, MD, USA

    Carl H. Smith

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© 2002 Springer-Verlag Berlin Heidelberg

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Devaney, J.E., Hagedorn, J.G. (2002). Discovery in Hydrating Plaster Using Machine Learning Methods. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_7

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  • DOI: https://doi.org/10.1007/3-540-36182-0_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00188-1

  • Online ISBN: 978-3-540-36182-4

  • eBook Packages: Springer Book Archive

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