On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series
This work addresses time-series classification and labeling efficiency for online monitoring applications, presenting an incremental approach by combining existing principal curve methods with new algorithmic components.
The paper tackles performance attenuation in recurrent neural networks due to time-span variations in time-series data by proposing principal curve-based classifiers that preserve time relativity, and introduces a deterministic selective sampling algorithm to reduce labeling costs, achieving unspecified improvements in handling time variations and reliability.
Considering the concept of time-dilation, there exist some major issues with recurrent neural Architectures. Any variation in time spans between input data points causes performance attenuation in recurrent neural network architectures. Principal curve-based classifiers have the ability of handling any kind of variation in time spans. In other words, principal curve-based classifiers preserve the relativity of time while neural network architecture violates this property of time. On the other hand, considering the labeling costs and problems in online monitoring devices, there should be an algorithm that finds the data points which knowing their labels will cause in better performance of the classifier. Current selective sampling algorithms have lack of reliability due to the randomness of the proposed algorithms. This paper proposes a classifier and also a deterministic selective sampling algorithm with the same computational steps, both by use of principal curve as their building block in model definition.