Publication Date:
1999
abstract:
A new algorithm, called Hamming Clustering
(HC), is proposed to extract a set of rules underlying a given classification problem. It is able
to reconstruct the and-or expression associated
with any Boolean function from a training set of
samples.
The basic kernel of the method is the generation of
clusters of input patterns that belong to the same
class and are close each other according to the
Hamming distance. Inputs are identified, which
do not influence the final output, thus automatically reducing the complexity of the final set of
rules.
Its application to artificial and real-world benchmarks has allowed a first evaluation of the performances exhibited by HC. In particular, in the diagnosis of breast cancer HC yielded a reduced set
of rules solving the associated classification problem.
Iris type:
04.01 Contributo in Atti di convegno
List of contributors:
Liberati, Diego; Muselli, Marco
Book title:
Proceedings of the Third International ICSC Symposia on Soft Computing (SOCO '99)