Obtaining interpretable fuzzy classification rules from medical data

For many application problems classifiers can be used to support a decision making process. In some domains-in areas like medicine especially-it is preferable not to use black box approaches. The user should be able to understand the classifier and to evaluate its results. Fuzzy rule based classifie...

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Bibliographic Details
Published in:Artificial intelligence in medicine 1999-06, Vol.16 (2), p.149-169
Main Authors: Nauck, Detlef, Kruse, Rudolf
Format: Article
Language:English
Subjects:
Algorithms
Classification
Data reduction
Decision Making, Computer-Assisted
Diagnosis
Fuzzy Logic
Fuzzy set theory
Fuzzy sets
Humans
Knowledge based systems
Learning
Learning algorithms
Learning systems
Medical computing
Medical informatics
Neural Networks (Computer)
Neuro-fuzzy system
Rule based classifier
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Summary:For many application problems classifiers can be used to support a decision making process. In some domains-in areas like medicine especially-it is preferable not to use black box approaches. The user should be able to understand the classifier and to evaluate its results. Fuzzy rule based classifiers are especially suitable, because they consist of simple linguistically interpretable rules and do not have some of the drawbacks of symbolic or crisp rule based classifiers. Classifiers must often be created from data by a learning process, because there is not enough expert knowledge to determine their parameters completely. A simple and convenient way to learn fuzzy classifiers from data is provided by neuro-fuzzy approaches. In this paper we discuss extensions to the learning algorithms of neuro-fuzzy classification (NEFCLASS), a neuro-fuzzy approach for data analysis that we have presented before. We present interactive strategies for pruning rules and variables from a trained classifier to enhance its readability, and demonstrate our approach on a small example.
ISSN:0933-3657
1873-2860
DOI:10.1016/S0933-3657(98)00070-0