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Reseach Article

An Evolutionary Multi Label Classification using Associative Rule Mining for Spatial Preferences

Published on None 2011 by J.Arunadevi, Dr.V.Rajamani
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 3
None 2011
Authors: J.Arunadevi, Dr.V.Rajamani
0dd7ebbe-8cda-4e04-8310-de6a95013c9a

J.Arunadevi, Dr.V.Rajamani . An Evolutionary Multi Label Classification using Associative Rule Mining for Spatial Preferences. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 3 (None 2011), 28-37.

@article{
author = { J.Arunadevi, Dr.V.Rajamani },
title = { An Evolutionary Multi Label Classification using Associative Rule Mining for Spatial Preferences },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 3 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 28-37 },
numpages = 10,
url = { /specialissues/ait/number3/2841-222/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A J.Arunadevi
%A Dr.V.Rajamani
%T An Evolutionary Multi Label Classification using Associative Rule Mining for Spatial Preferences
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 3
%P 28-37
%D 2011
%I International Journal of Computer Applications
Abstract

Multi-label spatial classification based on association rules with Multi objective genetic algorithms (MOGA) is proposed to deal with multiple class labels problem which is hard to settle by existing methods. In this paper we adapt problem transformation for the Multi label classification. We use Hybrid evolutionary algorithm for the optimization in the generation of spatial association rules, which addresses single label. MOGA is used to combine the single labels into multi labels with the conflicting objectives predictive accuracy and Comprehensibility. Finally we built the classifier with a sorting mechanism. The algorithm is executed and the results are compared with Decision trees and Neural network based classifiers, the proposed method out performs the existing.

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Index Terms

Computer Science
Information Sciences

Keywords

Multi label Classification Associative Classification MOGA HEA MOGA HEA