کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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407704 | 678166 | 2015 | 7 صفحه PDF | دانلود رایگان |
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.
Journal: Neurocomputing - Volume 149, Part A, 3 February 2015, Pages 86–92