NIPS*96 Rule Extraction W'shop
Robert Andrews
robert at fit.qut.edu.au
Wed Aug 21 01:42:18 EDT 1996
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FIRST CALL FOR PAPERS
NIPS*96 POST-CONFERENCE WORKSHOP
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RULE-EXTRACTION FROM TRAINED NEURAL NETWORKS
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Snowmass (Aspen), Colorado, USA
Fri December 6th, 1996
Robert Andrews & Joachim Diederich
Neurocomputing Research Centre
Queensland University of Technology
Brisbane 4001 Queensland, Australia
Fax: +61 7 864-1801
E-mail: robert at fit.qut.edu.au
E-mail: joachim at fit.qut.edu.au
Rule extraction can be defined as the process of deriving a symbolic
description of a trained Artificial Neural Network (ANN). Ideally the
rule extraction process results in a symbolic description which closely
mimics the behaviour of the network in a concise and comprehensible form.
The merits of including rule extraction techniques as an adjunct to
conventional Artificial Neural Network techniques include:
a) the provision of a 'User Explanation' capability;
b) improvement of the generalisation capabilities of ANN
solutions by allowing identification of regions of input
space not adequately represented;
c) data exploration and the induction of scientific theories
by the discovery and explicitation of previously unknown
dependencies and relationships in data sets;
d) knowledge acquistion for symbolic AI systems by overcoming
the knowledge engineering bottleneck;
e) the potential to contribute to the understanding of how
symbolic and connectionist approaches to AI can be
profitably integrated.
An ancillary problem to that of rule extraction from trained ANNs is that
of using the ANN for the `refinement' of existing rules within symbolic
knowledge bases. The goal in rule refinement is to use a combination of
ANN learning and rule extraction techniques to produce a `better' (ie a
`refined') set of symbolic rules which can then be applied back in the
original problem domain. In the rule refinement process, the initial rule
base (ie what may be termed `prior knowledge') is inserted into an ANN by
programming some of the weights. The rule refinement process then
proceeds in the same way as normal rule extraction viz (1) train the
network on the available data set(s); and (2) extract (in this case the
`refined') rules - with the proviso that the rule refinement process
may involve a number of iterations of the training phase rather than a
single pass.
The objective of this workshop is to provide a discussion platform for
researchers and practitioners interested in all aspects of rule
extraction from trained artificial neural networks. The workshop will
examine current techniques for providing an explanation component for
ANNs including rule extraction, extraction of fuzzy rules, rule
initialisation and rule refinement. Other topics for discussion include
computational complexity of rule extraction algorithms, criteria for
assessing rule quality, and issues relating to generalisation differences
between the ANN and the extracted rule set. The workshop will also
discuss ways in which ANNs and rule extraction techniques may be
profitably employed in commercial, industrial, and scientific application
areas.
The one day workshop will be a mixture of position papers and panel
discussions. Papers presented in the mini-conference sessions will be of
20 minutes duration with ample time for questions/discussions afterwards.
DISCUSSION POINTS FOR WORKSHOP PARTICIPANTS
1. Decompositional vs. learning approaches to rule-extraction from
ANNs - What are the advantages and disadvantages w.r.t. performance,
solution time, computational complexity, problem domain etc. Are
decompositional approaches always dependent on a certain ANN architecture?
2. Rule-extraction from trained neural networks v symbolic induction. What
are the relative strength and weaknesses?
3. What are the most important criteria for rule quality?
4. What are the most suitable representation languages for extracted
rules? How does the extraction problem vary across different languages?
5. What is the relationship between rule-initialisation (insertion) and
rule-extraction? For instance, are these equivalent or complementary
processes? How important is rule-refinement by neural networks?
6. Rule-extraction from trained neural networks and computational learning
theory.Is generating a minimal rule-set which mimics an ANN a hard problem?
7. Does rule-initialisation result in improved generalisation and faster
learning?
8. To what extent are existing extraction algorithms limited in their
applicability? How can these limitations be addressed?
9. Are there any interesting rule-extraction success stories? That is,
problem domains in which the application of rule-extraction methods has
resulted in an interesting or significant advance.
SUBMISSION OF WORKSHOP EXTENDED ABSTRACTS/PAPERS
Authors are invited to submit 3 copies of either an extended abstract or
full paper relating to one of the topic areas listed above. Papers should
be written in English in single column format and should be limited to no
more than eight, (8) sides of A4 paper including figures and references.
NIPS style files are available at
http://www.cs.cmu.edu/afs/cs/project/cnbc/nips/formatting/nips.sty
http://www.cs.cmu.edu/afs/cs/project/cnbc/nips/formatting/nips.tex
http://www.cs.cmu.edu/afs/cs/project/cnbc/nips/formatting/nips.ps
Please include the following information in an accompanying cover letter:
Full title of paper, presenting author's name, address, and telephone and
fax numbers, authors e-mail address.
Submission Deadline is October 7th,1996 with notification to authors by
31st October,1996.
For further information, inquiries, and paper submissions
please contact:
Robert Andrews
Queensland University of Technology
GPO Box 2434 Brisbane Q. 4001. Australia.
phone +61 7 864-1656
fax +61 7 864-1969
email robert at fit.qut.edu.au
More information about the NIPS*96 workshop series is available from:
WWW: http://www.fit.qut.edu.au/~robert/nips96.html
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