ELECTRICA

A DATA SELECTION METHOD FOR PROBABILISTIC NEURAL NETWORKS

1.

Elektronik ve Haberleşme Mühendisliği Bölümü Yıldız Teknik Üniversitesi Beşiktaş, İstanbul 34349 TURKEY

2.

Electronics and Communication Eng. Department Yildiz Technical University Besiktas, Istanbul 34349 TURKEY

ELECTRICA 2004; 4: 1137-1140
Read: 560 Downloads: 432 Published: 28 December 2011

In this paper, two performances increasing methods for datasets which have a nonuniform class distribution are presented. The methods are applied to probabilistic neural networks (PNN). Selection of a good training data is the most important issue. Therefore, a new data selection procedure including data exchange and data replication is proposed. After reaching the best accuracy by using the data exchange method, a data replication method is applied to the classes which have relatively less numbers of instances. The methods are applied to the Glass, Escheria Coli (E. coli) and Contact Lenses datasets, which have nonuniform class distributions and better accuracies than the reference works were achieved by PNN using these methods.

Files
EISSN 2619-9831