Enabling neuro-fuzzy classification to learn from partially labeled data

Due to their rather intuitive and understandable application fuzzy if-then rules are a popular basis for classifiers. The use of linguistic variables eases the readability and interpretability of the rule base. In many practical applications huge amounts of data are available. However, these are oft...

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Bibliographic Details
Main Authors: Klose, A., Kruse, R.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Computer science
Data mining
Fuzzy neural networks
Fuzzy sets
Fuzzy systems
Induction generators
Neural networks
Semisupervised learning
Supervised learning
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Summary:Due to their rather intuitive and understandable application fuzzy if-then rules are a popular basis for classifiers. The use of linguistic variables eases the readability and interpretability of the rule base. In many practical applications huge amounts of data are available. However, these are often unlabeled and the user must manually assign labels. The idea of semi-supervised learning is to use as much labeled data as available and try to additionally exploit the structure in the unlabeled data. We describe an approach to enable semi-supervised learning for (neuro-) fuzzy systems.
DOI:10.1109/FUZZ.2002.1005096