The University of Southampton
University of Southampton Institutional Repository

Decompositional rule extraction from support vector machines by active learning

Decompositional rule extraction from support vector machines by active learning
Decompositional rule extraction from support vector machines by active learning
Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. In this paper, we propose a new active learning-based approach (ALBA) to extract comprehensible rules from opaque SVM models. Through rule extraction, some insight is provided into the logics of the SVM model. ALBA extracts rules from the trained SVM model by explicitly making use of key concepts of the SVM: the support vectors, and the observation that these are typically close to the decision boundary. Active learning implies the focus on apparent problem areas, which for rule induction techniques are the regions close to the SVM decision boundary where most of the noise is found. By generating extra data close to these support vectors that are provided with a class label by the trained SVM model, rule induction techniques are better able to discover suitable discrimination rules. This performance increase, both in terms of predictive accuracy as comprehensibility, is confirmed in our experiments where we apply ALBA on several publicly available data sets.

alba, clustering, data mining, mining methods and algorithms, support vector machine, active learning, association rules, black box models, classification, rule extraction
1041-4347
178-191
Martens, D.
cda8c1d8-591a-402b-a8c4-800a02979bd7
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Martens, D.
cda8c1d8-591a-402b-a8c4-800a02979bd7
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9

Martens, D., Baesens, B. and Van Gestel, T. (2008) Decompositional rule extraction from support vector machines by active learning. IEEE Transactions on Knowledge and Data Engineering, 21 (2), 178-191. (doi:10.1109/TKDE.2008.131).

Record type: Article

Abstract

Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. In this paper, we propose a new active learning-based approach (ALBA) to extract comprehensible rules from opaque SVM models. Through rule extraction, some insight is provided into the logics of the SVM model. ALBA extracts rules from the trained SVM model by explicitly making use of key concepts of the SVM: the support vectors, and the observation that these are typically close to the decision boundary. Active learning implies the focus on apparent problem areas, which for rule induction techniques are the regions close to the SVM decision boundary where most of the noise is found. By generating extra data close to these support vectors that are provided with a class label by the trained SVM model, rule induction techniques are better able to discover suitable discrimination rules. This performance increase, both in terms of predictive accuracy as comprehensibility, is confirmed in our experiments where we apply ALBA on several publicly available data sets.

This record has no associated files available for download.

More information

Published date: 15 July 2008
Keywords: alba, clustering, data mining, mining methods and algorithms, support vector machine, active learning, association rules, black box models, classification, rule extraction

Identifiers

Local EPrints ID: 80428
URI: http://eprints.soton.ac.uk/id/eprint/80428
ISSN: 1041-4347
PURE UUID: 305b0e16-7bdd-4098-bdac-4f231f81949c
ORCID for B. Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

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

Export record

Altmetrics

Contributors

Author: D. Martens
Author: B. Baesens ORCID iD
Author: T. Van Gestel

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×