Abstract
The graph classification problem consists, given a weighted graph and a partial node labeling, in extending the labels to all nodes. In many real-world context, such as Gene Function Prediction, the partial labeling is unbalanced: positive labels are much less than negatives. In this paper we present a new neural algorithm for predicting labels in presence of label imbalance. This algorithm is based on a family of Hopfield networks, described by 2 continuous parameters and 1 discrete parameter, and it consists of two main steps: 1) the network parameters are learnt through a cost-sensitive optimization procedure based on local search; 2) a suitable Hopfield network restricted to unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of unlabeled nodes. An experimental analysis on real-world unbalanced data in the context of genome-wide prediction of gene functions show the effectiveness of the proposed approach.
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