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Minerva: Sequential covering for rule extraction

Minerva: Sequential covering for rule extraction
Minerva: Sequential covering for rule extraction
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.

classification, rule extraction, support vector machines
299-309
Huysmans, Johan
0a2bb876-e5bc-42c5-bcf1-18f089a6eec3
Setiono, Rudy
98ca7376-c02e-4f65-a2df-bb09cc0c6e6b
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Huysmans, Johan
0a2bb876-e5bc-42c5-bcf1-18f089a6eec3
Setiono, Rudy
98ca7376-c02e-4f65-a2df-bb09cc0c6e6b
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

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), 299-309. (doi:10.1109/TSMCB.2007.912079).

Record type: Article

Abstract

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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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
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 24 Mar 2010
Last modified: 14 Mar 2024 02:49

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Contributors

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

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