Abstract
Classifiers are often opaque and cannot easily be inspected to gain understanding of which factors are of importance. We propose an efficient iterative algorithm to find the attributes and dependencies used by any classifier when making predictions. The performance and utility of the algorithm is demonstrated on two synthetic and 26 real-world datasets, using 15 commonly used learning algorithms to generate the classifiers. The empirical investigation shows that the novel algorithm is indeed able to find groupings of interacting attributes exploited by the different classifiers. These groupings allow for finding similarities among classifiers for a single dataset as well as for determining the extent to which different classifiers exploit such interactions in general.
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Notes
The algorithm can be downloaded from https://bitbucket.org/aheneliu/goldeneye/ (accessed 7 July 2014) or easily installed using the command install_bitbucket from the devtools package (Wickham and Chang 2014) as follows: install_bitbucket(repo = “goldeneye”, username = “aheneliu”).
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Acknowledgments
AH and KP were partly supported by the Revolution of Knowledge Work project, funded by Tekes. HB and LA were partly supported by the project High-Performance Data Mining for Drug Effect Detection at Stockholm University, funded by Swedish Foundation for Strategic Research under grant IIS11-0053.
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Responsible editor: Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo.
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Henelius, A., Puolamäki, K., Boström, H. et al. A peek into the black box: exploring classifiers by randomization. Data Min Knowl Disc 28, 1503–1529 (2014). https://doi.org/10.1007/s10618-014-0368-8
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DOI: https://doi.org/10.1007/s10618-014-0368-8