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Published in:
2013 | OriginalPaper | Chapter
Bias-Guided Random Walk for Network-Based Data Classification
Authors : Thiago Henrique Cupertino, Liang Zhao
Published in: Advances in Neural Networks – ISNN 2013
Publisher: Springer Berlin Heidelberg
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This paper presents a new network-based classification technique using limiting probabilities from random walk theory. Instead of using a traditional heuristic to classify data relying on physical features such as similarity or density distribution, it uses a concept called ease of access. By means of an underlying network, in which nodes represent states for the random walk process, unlabeled instances are classified with the label of the most easily reached class. The limiting probabilities are used as a measure for the ease of access by taking into account the biases provided by an unlabeled instance in a specific adjacency matrix weight composition. In this way, the technique allows data classification from a different viewpoint. Simulation results suggest that the proposed scheme is competitive with current and well-known classification algorithms.