Please use this identifier to cite or link to this item:
|Title:||Minerva: Sequential covering for rule extraction|
Support vector machines
|Citation:||Huysmans, J., Setiono, R., Baesens, B., Vanthienen, J. (2008). Minerva: Sequential covering for rule extraction. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38 (2) : 299-309. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2007.912079|
|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. © 2008 IEEE.|
|Source Title:||IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 18, 2018
WEB OF SCIENCETM
checked on Oct 2, 2018
checked on Aug 31, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.