publication . Article . 2014

A peek into the black box: exploring classifiers by randomization

Kai Puolamäki; Panagiotis Papapetrou;
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  • Published: 23 Jul 2014 Journal: Data Mining and Knowledge Discovery, volume 28, pages 1,503-1,529 (issn: 1384-5810, eissn: 1573-756X, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
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 datase...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Computer Networks and Communications, Information Systems, Computer Science Applications
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