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

Artificial Intelligence Techniques - Novel Approaches & Practical Applications
© 2011 by IJCA Journal
Number 3 - Article 1
Year of Publication: 2011

J.Arunadevi and Dr.V.Rajamani. An Evolutionary Multi Label Classification using Associative Rule Mining for Spatial Preferences. IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications (3):28–37, 2011. Full text available. BibTeX

	author = {J.Arunadevi and Dr.V.Rajamani},
	title = {An Evolutionary Multi Label Classification using Associative Rule Mining for Spatial Preferences},
	journal = {IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications},
	year = {2011},
	number = {3},
	pages = {28--37},
	note = {Full text available}


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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