NDLI: Discovery of comprehensible symbolic rules in a neural network

Content Provider	IEEE Xplore Digital Library
Author	Avner, S.
Copyright Year	1995
Description	Author affiliation: LAFORIA-CNRS URA, Paris VI Univ., France (Avner, S.)
Abstract	In this paper, we introduce a system that extracts comprehensible symbolic rules from a multilayer perceptron. Once the network has been trained in the usual manner, the training set is presented again, and the actual activations of the units recorded. Logical rules, corresponding to the logical combinations of the incoming signals, are extracted at each activated unit. This procedure is used for all examples belonging to the training set. Thus we obtain a set of rules which account for all logical steps taken by the network to process all known input patterns. Furthermore, we show that if some symbolic meaning were associated to every input unit, then the hidden units, which have formed concepts in order to deal with recurrent features in the input data, possess some symbolic meaning tool. Our algorithm allows the recognition or the understandability of these concepts: they are found to be reducible to conjunctions and negations of the human input concepts. Our rules can also be recombined in different ways, thus constituting some limited but sound generalization of the training set. Neural networks could learn concepts about domains where little theory was known but where many examples were available. Yet, because their knowledge was stored in the synaptic strengths under numerical form, it was difficult to comprehend what they had discovered. This system therefore provides some means of accessing the information contained inside the network.<>
Starting Page	64
Ending Page	71
File Size	675799
Page Count	8
File Format	PDF
ISBN	0818671165
DOI	10.1109/INBS.1995.404278
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-05-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Neural networks Neurons Humans Multilayer perceptrons Brain modeling Cognition Electronic mail Biological neural networks Multi-layer neural network
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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