An Evolutionary Multi Label Classification Using Associative Rule Mining for Spatial Preferences
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, the authors adapt problem transformation for the Multi label classification. They 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, they built the classifier with a sorting mechanism.