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Automatic Threshold Selection using PSO for GA based Duplicate Record Detection

by K. Deepa, R. Rangarajan, M. Senthamil Selvi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 4
Year of Publication: 2013
Authors: K. Deepa, R. Rangarajan, M. Senthamil Selvi
10.5120/10068-4674

K. Deepa, R. Rangarajan, M. Senthamil Selvi . Automatic Threshold Selection using PSO for GA based Duplicate Record Detection. International Journal of Computer Applications. 62, 4 ( January 2013), 22-27. DOI=10.5120/10068-4674

@article{ 10.5120/10068-4674,
author = { K. Deepa, R. Rangarajan, M. Senthamil Selvi },
title = { Automatic Threshold Selection using PSO for GA based Duplicate Record Detection },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 4 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number4/10068-4674/ },
doi = { 10.5120/10068-4674 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:47.111439+05:30
%A K. Deepa
%A R. Rangarajan
%A M. Senthamil Selvi
%T Automatic Threshold Selection using PSO for GA based Duplicate Record Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 4
%P 22-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Normally setting the threshold is an important issue in applications where the similarity functions are used and it relies more on human intervention. The proposed work addressed two issues : first to find the optimal equation using Genetic Algorithm (GA) and next it adopts an intelligence algorithm, Particle Swarm Optimization (PSO) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. Restaurant and CORA data repository are used to analyze the proposed algorithm and the performance of the proposed algorithm is compared against marlin method and the genetic programming with the help of evaluation metrics.

References
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Computer Science
Information Sciences

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