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 diff...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2008-04, Vol.38 (2), p.299-309
Main Authors: Huysmans, J., Setiono, R., Baesens, B., Vanthienen, J.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Benchmarking
Classification
Cybernetics
Decision Support Techniques
Decision trees
Extraction
Human
Humans
Immune system
Information management
Information Storage and Retrieval - methods
Machine learning
Machine learning algorithms
Medical diagnosis
Neural networks
Opacity
Pattern Recognition, Automated - methods
rule extraction
Studies
Support vector machines
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Summary: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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2007.912079