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This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification.

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered.

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    Date Created
    • 2011
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  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2011
      Note type
      thesis
    • Includes bibliographical references (p
    • Field of study: Industrial engineering

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    Houtao Deng

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