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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2010: Research and Development in Intelligent Systems XXVII pp 379–392Cite as

Selecting Features in Origin Analysis

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Abstract

When applying a machine-learning approach to develop classifiers in a new domain, an important question is what measurements to take and how they will be used to construct informative features. This paper develops a novel set of machine-learning classifiers for the domain of classifying files taken from software projects; the target classifications are based on origin analysis. Our approach adapts the output of four copy-analysis tools, generating a number of different measurements. By combining the measures and the files on which they operate, a large set of features is generated in a semi-automatic manner. After which, standard attribute selection and classifier training techniques yield a pool of high quality classifiers (accuracy in the range of 90%), and information on the most relevant features.

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Author information

Authors and Affiliations

  1. University of Hertfordshire, College Lane, Hatfield, Herts, AL10 9AB, UK

    Pam Green, Peter C.R. Lane, Austen Rainer & Sven-Bodo Scholz

Authors
  1. Pam Green
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  2. Peter C.R. Lane
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  3. Austen Rainer
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  4. Sven-Bodo Scholz
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Corresponding authors

Correspondence to Pam Green , Peter C.R. Lane , Austen Rainer or Sven-Bodo Scholz .

Editor information

Editors and Affiliations

  1. Dept. Computer Science and, Software Engineering, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , Faculty of Technology, De Montford University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Adrian Hopgood

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© 2011 Springer-Verlag London Limited

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Green, P., Lane, P.C., Rainer, A., Scholz, SB. (2011). Selecting Features in Origin Analysis. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_29

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  • DOI: https://doi.org/10.1007/978-0-85729-130-1_29

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-129-5

  • Online ISBN: 978-0-85729-130-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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