An enhanced noise resilient K-associated graph classifier
We propose a non-parametric, noise resilient, graph-based classification algorithm. We employ relational data such as the degree of relevancy. We combine smaller components together to build larger ones. The algorithm is less noise sensitive than SVM and Decision Tree. The algorithm shows a superior performance in presence of different levels of noise.
k-associated graph graph-based classifier noisy samples