Discovery of Predictive Neighborly Rules from Neighborhood Systems

Ray R. Hashemi; Azita Bahrami; Mark Smith; Nicholas R. Tyler; Matthew Antonelli; Sean Clapp

Discovery of Predictive Neighborly Rules from Neighborhood Systems

Ray R. Hashemi, Azita Bahrami, Mark Smith, Nicholas R. Tyler, Matthew Antonelli, Sean Clapp

Computer Science

Research output: Contribution to book or proceeding › Chapter

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.

Original language	American English
Title of host publication	Proceedings of the International Conference on Information and Knowledge Engineering (IKE)
State	Published - Jul 1 2013

Disciplines

Computer Sciences

Keywords

Expanded Record Tree
Machine Learning
Neighborhood System
Record Tree
Workable Neighborhood System
and Neighborly Rules

Access to Document

https://www.proquest.com/docview/1491735549/fulltextPDF/7C23B7C00F5A44B9PQ/1?accountid=11225

Cite this

@inbook{ac3691e8168e4988994d6e6d9567506d,

title = "Discovery of Predictive Neighborly Rules from Neighborhood Systems",

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.",

keywords = "Expanded Record Tree, Machine Learning, Neighborhood System, Record Tree, Workable Neighborhood System, and Neighborly Rules",

author = "Hashemi, {Ray R.} and Azita Bahrami and Mark Smith and Tyler, {Nicholas R.} and Matthew Antonelli and Sean Clapp",

year = "2013",

month = jul,

day = "1",

language = "American English",

booktitle = "Proceedings of the International Conference on Information and Knowledge Engineering (IKE)",

}

TY - CHAP

T1 - Discovery of Predictive Neighborly Rules from Neighborhood Systems

AU - Hashemi, Ray R.

AU - Bahrami, Azita

AU - Smith, Mark

AU - Tyler, Nicholas R.

AU - Antonelli, Matthew

AU - Clapp, Sean

PY - 2013/7/1

Y1 - 2013/7/1

N2 - 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.

AB - 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.

KW - Expanded Record Tree

KW - Machine Learning

KW - Neighborhood System

KW - Record Tree

KW - Workable Neighborhood System

KW - and Neighborly Rules

UR - https://digitalcommons.georgiasouthern.edu/compsci-facpubs/240

UR - https://www.proquest.com/docview/1491735549/fulltextPDF/7C23B7C00F5A44B9PQ/1?accountid=11225

M3 - Chapter

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

ER -

Discovery of Predictive Neighborly Rules from Neighborhood Systems

Abstract

Disciplines

Keywords

Access to Document

Other files and links

Fingerprint

Cite this