Multi Label Spatial Semi Supervised Classification using Spatial Associative Rule Mining and Evolutionary Algorithms

Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. 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. Semi supervised learning is done through the process of rule cover clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is simulated and the results are compared with MOGA based associative classifier, which out performs the existing.