Design of supervised classifiers using Boolean Neural Networks

Srinivas Gazula, Mansur R. Kabuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present two supervised pattern classifiers designed using Boolean Neural Networks (BNN). They are (a) Nearest-to-an-Exemplar (NTE) and (b) Boolean K-Nearest Neighbor (BKNN) classifier. The classifiers use the idea of Radius of Attraction (ROA) to achieve their goal. Patterns are classified by constructing hyper spheres in feature space with the exemplar nodes as the centers for the NTE classifier and with nodes representing each of the training patterns as centers in the BKNN. The radii of these hyper spheres are to be adapted to the problem at hand. Both these classifiers are tested with well-known data sets of binary as well as continuous feature values. Results obtained are comparable with those obtained by similar existing classifiers.

Original languageEnglish
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
EditorsC.H. Dagli, L.I. Burke, Y.C. Shin
Place of PublicationFairfield, NJ, United States
PublisherASME
Pages5-13
Number of pages9
Volume2
StatePublished - Dec 1 1992
EventProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
Duration: Nov 15 1992Nov 18 1992

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
CitySt.Louis, MO, USA
Period11/15/9211/18/92

ASJC Scopus subject areas

  • Software

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  • Cite this

    Gazula, S., & Kabuka, M. R. (1992). Design of supervised classifiers using Boolean Neural Networks. In C. H. Dagli, L. I. Burke, & Y. C. Shin (Eds.), Intelligent Engineering Systems Through Artificial Neural Networks (Vol. 2, pp. 5-13). ASME.