Random Pairwise Shapelets Forest
This is an incremental improvement for time series classification, addressing specific bottlenecks in shapelet-based methods.
The paper tackles the inefficiency and limited accuracy of shapelet-based random forests by introducing Random Pairwise Shapelets Forest (RPSF), which uses pairs of shapelets to omit threshold searching and improve node information, resulting in enhanced accuracy and training speed.
Shapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into accurate and fast random forest. However, it shows several limitations. First, random shapelet forest requires a large training cost for split threshold searching. Second, a single shapelet provides limited information for only one branch of the decision tree, resulting in insufficient accuracy and interpretability. Third, randomized ensemble causes interpretability declining. For that, this paper presents Random Pairwise Shapelets Forest (RPSF). RPSF combines a pair of shapelets from different classes to construct random forest. It omits threshold searching to be more efficient, includes more information for each node of the forest to be more effective. Moreover, a discriminability metric, Decomposed Mean Decrease Impurity (DMDI), is proposed to identify influential region for every class. Extensive experiments show RPSF improves the accuracy and training speed of shapelet-based forest. Case studies demonstrate the interpretability of our method.