Discovery of Predictive Neighborly Rules from Neighborhood Systems

Authors

Ray R. Hashemi, Georgia Southern UniversityFollow
Azita Bahrami, IT Consultation CompanyFollow
Mark Smith, University of Central Arkansas
Nicholas R. Tyler, Armstrong Atlantic State UniversityFollow
Matthew Antonelli, Georgia Southern UniversityFollow
Sean Clapp, Armstrong Atlantic State University

Document Type

Contribution to Book

Publication Date

7-1-2013

Publication Title

Proceedings of the International Conference on Information and Knowledge Engineering (IKE)

Abstract

Georgia Southern University faculty member Ray R. Hashemi authored "Discovery of Predictive Neighborly Rules from Neighborhood Systems" in International Conference on Information and Knowledge Engineering (IKE'13).

The use of "data closeness" for clustering, concept generalization, and imprecise query processing has been frequently reported in the literature. In this article, however, the authors have introduced the use of "data closeness" for building a prediction tool. To do so, they: (1) Generate the workable neighborhood system for every record, Ri, of a training set, (2) build and expand the "record tree" for Ri, using its workable neighborhood system, (3) Extract a neighborly rule from each expanded record tree, and (4) Use the rules for prediction. The empirical results revealed that, the predictive power of the neighborly rules is comparable with that of ID3 and Rough Sets.

Recommended Citation

Hashemi, Ray R., Azita Bahrami, Mark Smith, Nicholas R. Tyler, Matthew Antonelli, Sean Clapp. 2013. "Discovery of Predictive Neighborly Rules from Neighborhood Systems." Proceedings of the International Conference on Information and Knowledge Engineering (IKE): 119-125 Las Vegas, NV.
https://digitalcommons.georgiasouthern.edu/compsci-facpubs/240