RIS ID
84872
Abstract
This paper introduces a novel cost sensitive weighted samples approach to a cascade of Graph Neural Networks for learning from imbalanced data in the graph structured input domain. This is shown to be very effective in addressing the effects of imbalanced data distribution on learning systems. The proposed idea is based on a weighting mechanism which forces the network to encode misclassified graphs (or nodes) more strongly. We evaluate the approach through an application to the well known Web spam detection problem, and demonstrate that the general-ization performance is improved as a result. Indeed the results obtained reported in this paper are the best reported so far for both datasets.
Grant Number
ARC/DP110103376
Additional Grant Number
COinS
Publication Details
Van Tuc, N., Tsoi, A. & Hagenbuchner, M. (2013). Cost-sensitive cascade Graph Neural Networks. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 527-532). Bruges, Belgium: i6doc.com.