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A peek into the black box: exploring classifiers by randomization
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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2014 (English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 28, no 5-6, 1503-1529 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 28, no 5-6, 1503-1529 p.
Keyword [en]
Classifiers, Randomization
National Category
Information Systems, Social aspects Computer and Information Science
Identifiers
URN: urn:nbn:se:su:diva-107795DOI: 10.1007/s10618-014-0368-8ISI: 000341085700014OAI: oai:DiVA.org:su-107795DiVA: diva2:753016
Note

AuthorCount:5;

Available from: 2014-10-06 Created: 2014-09-29 Last updated: 2017-12-05Bibliographically approved

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Boström, HenrikAsker, LarsPapapetrou, Panagiotis
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