Cupertino, Thiago H.
Carneiro, Murillo G.
Total Authors: 3
 Univ Sao Paulo, Inst Math Sci & Comp, BR-13560970 Sao Carlos, SP - Brazil
 Univ Sao Paulo, Sch Philosophy Sci & Literature Ribeirao Preto, BR-14040900 Ribeirao Preto, SP - Brazil
 Univ Fed Uberlandia, Fac Comp, BR-38400902 Uberlandia, MG - Brazil
Total Affiliations: 3
FEB 3 2015.
Web of Science Citations:
We propose a new supervised classification technique which considers the ease of access of unlabeled instances to training classes through an underlying network. The training data set is used to construct a network, in which instances (nodes) represent the states that a random walker visits, and the network link structure is modified by performing a link weight composition between the unlabeled instance bias and the initial network link weights. Different from traditional classification heuristics, which divide the training data set into subspaces, the proposed scheme uses random walk limiting probabilities to measure the limiting state transitions among training nodes. An unlabeled instance receives the label of the class that is most easily reached by the random walker, that is, the limiting transition to that class is large. Simulation results suggest that the proposed technique is comparable to some well-known classification techniques. (C) 2014 Elsevier B.V. All rights reserved. (AU)