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Considering the concept of time-dilation, there exist some major issues withrecurrent neural Architectures. Any variation in time spans between input datapoints causes performance attenuation in recurrent neural networkarchitectures. Principal curve-based classifiers have the ability of handlingany kind of variation in time spans. In other words, principal curve-basedclassifiers preserve the relativity of time while neural network architectureviolates this property of time. On the other hand, considering the labelingcosts and problems in online monitoring devices, there should be an algorithmthat finds the data points which knowing their labels will cause in betterperformance of the classifier. Current selective sampling algorithms have lackof reliability due to the randomness of the proposed algorithms. This paperproposes a classifier and also a deterministic selective sampling algorithmwith the same computational steps, both by use of principal curve as theirbuilding block in model definition.

On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series