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Minerva: Sequential covering for rule extraction

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Various benchmarking studies have shown that artificial neural networks and support vector machines often have superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the reasoning behind these models' decisions. Various rule extraction (RE) techniques have been proposed to overcome this opacity restriction. These techniques are able to represent the behavior of the complex model with a set of easily understandable rules. However, most of the existing RE techniques can only be applied under limited circumstances, e.g., they assume that all inputs are categorical or can only be applied if the black-box model is a neural network. In this paper, we present Minerva, which is a new algorithm for RE. The main advantage of Minerva is its ability to extract a set of rules from any type of black-box model. Experiments show that the extracted models perform well in comparison with various other rule and decision tree learners.

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Citation

Huysmans, Johan, Setiono, Rudy, Baesens, Bart and Vanthienen, Jan (2008) Minerva: Sequential covering for rule extraction IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 38, (2), pp. 299-309. (doi:10.1109/TSMCB.2007.912079).

More information

Published date: 7 March 2008
Keywords: classification, rule extraction, support vector machines

Identifiers

Local EPrints ID: 80431
URI: http://eprints.soton.ac.uk/id/eprint/80431
PURE UUID: 4c757ec7-6b84-4a9a-8c6a-483afcf10f17

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Date deposited: 24 Mar 2010
Last modified: 18 Jul 2017 23:13

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Contributors

Author: Johan Huysmans
Author: Rudy Setiono
Author: Bart Baesens
Author: Jan Vanthienen

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