Coupled similarity analysis in supervised learning
Chaofan Liu · UTS ePRESS (University of Technology Sydney) · 2015
to their natural co-occurrence.This similarity reflects the distance of the different classes.By integrating this similarity with the multi-label kNN algorithm, we improve the performance significantly.Evaluated over three commonly used verification criteria for multi-label classifiers, our proposed coupled multi-label classifier outperforms the ML-kNN, BR-kNN and even IBLR.The result indicates that our supposed coupled label similarity is appropriate for multi-label learning problems and can work more effectively compared to other methods.All the classifiers analyzed in this thesis are based on our coupling similarity (or distance), and applied to different tasks in supervised learning.The performance of these models is examined by widely used verification criteria, such as ROC, Accuracy Rate, Average Precision and Hamming Loss.This thesis provides insightful knowledge for investors to find the inner relationship between features in supervised learning tasks.xii