A new constructive learning algorithm, called Hamming Clustering (HC), for binary neural networks is proposed. It is able to generate a set of rules in if-then form underlying an unknown classification problem starting from a training set of samples. The performance of HC has been evaluated through a variety of artificial and realworld benchmarks. In particular, its application in the diagnosis of breast cancer has led to the derivation of a reduced set of rules solving the associated classification problem.

Rule extraction from binary neural networks

M Muselli;D Liberati
1999

Abstract

A new constructive learning algorithm, called Hamming Clustering (HC), for binary neural networks is proposed. It is able to generate a set of rules in if-then form underlying an unknown classification problem starting from a training set of samples. The performance of HC has been evaluated through a variety of artificial and realworld benchmarks. In particular, its application in the diagnosis of breast cancer has led to the derivation of a reduced set of rules solving the associated classification problem.
1999
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
0-85296-721-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/221016
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