Learning Time-series Shapelets via Supervised Feature Selection
Akihiro Yamaguchi, Ken Ueno · Society for Industrial and Applied Mathematics eBooks · 2021
Shapelets are time-series segments effective for classifying time-series instances.Joint learning of both classifiers and shapelets has been studied in recent years because this approach provides both superior classification performance and interpretable results.However, the optimization formulation is nonconvex, so bad local minima must be avoided.Very recently, this issue has been tackled by introducing Self-Paced Learning (SPL) into these methods.With the aim of intelligently discovering initial shapelets, we introduce two steps into this binary classification method so as to consistently optimize the same loss function of interest: optimizing discovery of discriminative initial shapelets from many time-series segments by using supervised feature selection, and jointly optimizing shapelets, model parameters, and latent instance weights in SPL.Using UCR datasets, we demonstrate improved area under the curve, and interpretability of shapelets where the number of shapelets is small.