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Identification of Unbalanced Warship Designs Using Multivariate Outlier Detection Procedures

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The paper aims to identify unsuccessful warship designs using advanced outlier detection procedures. Therefore, we generate a data set covering information on general characteristics, offensive/defensive power, and sensor capabilities of more than 800 different classes of (surface) warships from the cold war era to the current era (including planned future classes). On this high-dimensional data set we apply two approaches, one based on self-organizing maps (i.e., a special kind of artificial neural network, Liebscher et al. 2012), the other based on minimum spanning trees (Kirschstein et al. 2012). Both methods can identify outliers (i.e., “unbalanced” designs) and reveal cluster structures in the data. In a second step we compare the outliers' characteristics to a reference group (which is also determined in the first step) to decide whether they are “good” or “bad” ones.

Document Type: Research Article

DOI: http://dx.doi.org/10.5711/1082598317131

Publication date: March 1, 2012

More about this publication?
  • Military Operations Research is the leading peer-reviewed journal publishing articles in the fields that describe operations research (OR) methodologies and theories used in key military applications.

    MOR specifically invites papers that are significant military OR applications. Of particular interest are papers that present case studies showing innovative OR applications, apply OR to major policy issues, introduce interesting new problem areas, highlight educational issues, and document the history of military OR.
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