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        Selecting Features in Origin Analysis

        Author
        Green, P. D.
        Lane, P.C.R.
        Rainer, A.
        Scholz, S.
        Attention
        2299/9655
        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.
        Publication date
        2010
        Published in
        Research and Development in Intelligent Systems XXVII, Incorporating Applications and Innovations in Intelligent Systems XVIII,
        Other links
        http://hdl.handle.net/2299/9655
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