A Sample Dependent Decision Fusion Algorithm for Graph-based Semi-supervised Learning

On many occasions, the evaluation of a phenomenon based on a single feature could not solely be resulted in comprehensive and accurate results. Moreover, even if we have several features, we don’t know in advance, which feature offers a better description of the phenomenon. Thus, selecting the best features and especially their combination could lead to better results. An affinity graph is a tool that can describe the relationship between the samples. In this paper, we proposed a graph-based sample-based ranking method that sorts the graphs based on six proposed parameters. The sorting is performed such that the graphs at the top of the list have better performance compared to the graphs at the bottom. Furthermore, we propose a fusion method to merge the information of various features and improve the accuracy of label propagation. Moreover, a method is proposed for parameter optimizations and the ultimate decision fusion. The experimental results indicate that the proposed scheme, apart from correctly ranking the graphs according to their accuracy, in the fusion step, increases the accuracy compared to the use of a single feature.

Keywords:

Affinity Graph , Decision Fusion , Label Propagation , Multiple Features