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Inductive Discovery by Machine Learning for Identification of Structural Models

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Conceptual Modeling (ER 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11157))

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Abstract

Automatic extraction of structural models interferes with the deductive research method in information systems research. Nonetheless it is tempting to use a statistical learning method for assessing meaningful relations between structural variables given the underlying measurement model. In this paper, we discuss the epistemological background for this method and describe its general structure. Thereafter this method is applied in a mode of inductive confirmation to an existing data set that has been used for evaluating a deductively derived structural model. In this study, a range of machine learning model classes is used for statistical learning and results are compared with the original model.

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Authors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Wolfgang Maass & Iaroslav Shcherbatyi

Authors
  1. Wolfgang Maass
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  2. Iaroslav Shcherbatyi
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Corresponding author

Correspondence to Iaroslav Shcherbatyi .

Editor information

Editors and Affiliations

  1. Lucentia, University of Alicante, Alicante, Spain

    Juan C. Trujillo

  2. Miami University, Oxford, OH, USA

    Karen C. Davis

  3. Renmin University of China, Beijing, China

    Xiaoyong Du

  4. Northwestern Polytechnical University, Xian, China

    Zhanhuai Li

  5. Department of Computer Science, National University of Singapore, Singapore, Singapore

    Tok Wang Ling

  6. Department of Computer Science and Technology, Tsinghua University, Beijing, Beijing, China

    Guoliang Li

  7. National University of Singapore, Singapore, Singapore

    Mong Li Lee

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Maass, W., Shcherbatyi, I. (2018). Inductive Discovery by Machine Learning for Identification of Structural Models. In: Trujillo, J., et al. Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11157. Springer, Cham. https://doi.org/10.1007/978-3-030-00847-5_40

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  • DOI: https://doi.org/10.1007/978-3-030-00847-5_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00846-8

  • Online ISBN: 978-3-030-00847-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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